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Introduction: This study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors. Methods: The MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD). Results: When applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth. Discussion: FusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance. [ABSTRACT FROM AUTHOR]

Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from Magnetic Resonance Imaging (MRI). However, existing methods used for glioma segmentation usually rely solely on either local or global features and perform poorly in terms of capturing and exploiting critical information from tumor volume features. Herein, we propose a local and global dual transformer with an attentional supervision U-shape network called DTASUnet, which is purposed for glioma segmentation. First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. We also designed a 3D channel and spatial attention supervision module to guide the network, allowing it to capture key information in volumetric features more accurately during the training process. In the BraTS 2018 validation set, the average Dice scores of DTASUnet for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions were 0.845, 0.905, and 0.808, respectively. These results demonstrate that DTASUnet has utility in assisting clinicians with determining the location of gliomas to facilitate more efficient and accurate brain surgery and diagnosis. [ABSTRACT FROM AUTHOR]

Background and objective: Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long‐range relationships between voxels cannot be captured due to the localized character of the convolution procedure. Method: Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context‐aware module was introduced to capture the long‐range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. Result: Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state‐of‐the‐art methods. Conclusion: The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis. [ABSTRACT FROM AUTHOR]

Abstract Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from Magnetic Resonance Imaging (MRI). However, existing methods used for glioma segmentation usually rely solely on either local or global features and perform poorly in terms of capturing and exploiting critical information from tumor volume features. Herein, we propose a local and global dual transformer with an attentional supervision U-shape network called DTASUnet, which is purposed for glioma segmentation. First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. We also designed a 3D channel and spatial attention supervision module to guide the network, allowing it to capture key information in volumetric features more accurately during the training process. In the BraTS 2018 validation set, the average Dice scores of DTASUnet for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions were 0.845, 0.905, and 0.808, respectively. These results demonstrate that DTASUnet has utility in assisting clinicians with determining the location of gliomas to facilitate more efficient and accurate brain surgery and diagnosis.

This study explores the use of efficient deep learning algorithms for segmenting lower grade gliomas (LGG) in medical images. It evaluates various pre-trained atrous-convolutional architectures and U-Nets, proposing a novel transformer-based approach that surpasses traditional methods. DeepLabV3+ with MobileNetV3 backbone achieved the best results among pre-trained models, but the transformer-based approach excelled with superior segmentation accuracy and efficiency. Transfer learning significantly enhanced model performance on the LGG dataset, even with limited training samples, emphasizing the importance of selecting appropriate pre-trained models. The transformer-based method offers advantages such as efficient memory usage, better generalization, and the ability to process images of arbitrary sizes, making it suitable for clinical applications. These findings suggest that advanced deep learning techniques can improve diagnostic tools for LGG and potentially other cancers, highlighting the transformative impact of deep learning and transfer learning in medical image segmentation.

In the field of brain tumor segmentation, models based on CNNs and transformer have received a lot of attention. However, CNNs have limitations in long-range modeling, and although transformers can model at long distances, they have quadratic computational complexity. Recently, state space models (SSM), exemplified by the Mamba model, can achieve linear computational complexity and are adept at long-distance interactions. In this paper, we propose pocket convolution Mamba (P-BTS), which utilizes the PocketNet paradigm, SSM, and patch contrastive learning to achieve an efficient segmentation model. Specifically, the encoder follows the PocketNet paradigm, the SSM is used at the highest level of the model encoder to capture rich semantic information, and patch contrastive learning is achieved through the results of dual-stream data. Meanwhile, we designed a spatial channel attention (SCA) module to enhance control over spatial channels, and a feature complement module (FCM) to facilitate the interaction between low-level features and high-level semantic information. We conducted comprehensive experiments on the BraTS2018 and BraTS2019 datasets, and the results show that P-BTS has excellent segmentation performance. Our code has been released at . [ABSTRACT FROM AUTHOR]

Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part.

Abstract The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Manual segmentation of brain tumors using structural magnetic resonance imaging (MRI) is an arduous and time-consuming task. Therefore, automatic and robust segmentation will considerably influence neuro-oncological clinical trials by reducing excessive manual annotation time. Herein, we propose a deep learning model that automatically segments brain tumors even in cases of missing MRI sequences, which are common in practical clinical settings. To address this issue, we enhance a generative adversarial network (GAN) by incorporating a squeeze-and-excitation (SE) attention module into its generator and a PatchGAN into its discriminator. The SE module recalibrates channel responses by explicitly modeling interdependencies, enabling the network to focus on critical regions such as tumor areas. Our proposed generative model is optimized using a combination of adversarial, structural similarity, and mean absolute error losses to synthesize missing MRI sequences more effectively. This enhancement allows our model to synthesize the missing MRI sequence (fluid attenuated inversion recovery [FLAIR]) by leveraging information from other available sequences (T1-weighted, T2-weighted, or contrast-enhanced T1-weighted [T1ce]). For the segmentation task, we employ an optimized nnU-Net model, which is trained using existing sequences and evaluated using both available and synthesized sequences (including missing ones), mimicking real-world scenarios where often only limited MRI sequences are available or usable. Our findings reveal a notable enhancement in brain tumor segmentation, as indicated by a significant increase in overall the Dice similarity coefficient (DSC) from 0.688% (when FLAIR is missing) to 0.873% (when using synthesized FLAIR derived from T2). This improvement brings the segmentation performance closer to what was achieved when real FLAIR was available, where the DSC reaches 0.901%. Moreover, our synthesizing model was also tested on two additional datasets: the BraTS 2020 validation set and BraTS Africa 2023 training set, which produces results comparable to those of BraTS 2021, thereby proving its robustness and generalizability. In addition, the resulting tumor segmentations are subsequently employed to assess the response to treatment in cases where all sequences were available and when synthesis was employed, according to response assessment in neuro-oncology criteria.

Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part. [ABSTRACT FROM AUTHOR]

The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative approach to significantly enhance the precision and interpretability of brain tumor detection and segmentation. Our method ingeniously integrates the cutting-edge capabilities of the ChatGPT chatbot interface with a state-of-the-art multi-modal convolutional neural network (CNN). Tested rigorously on the BraTS dataset, our method showcases unprecedented performance, outperforming existing techniques in terms of both accuracy and efficiency, with an impressive Dice score of 0.89 for tumor segmentation. By seamlessly integrating ChatGPT, our model unveils deep-seated insights into the intricate decision-making processes, providing researchers and physicians with invaluable understanding and confidence in the results. This groundbreaking fusion holds immense promise, poised to revolutionize the landscape of medical imaging, with far-reaching implications for clinical practice and research. Our study exemplifies the transformative potential achieved through the synergistic combination of multi-modal CNNs and natural language processing, paving the way for remarkable advancements in brain tumor detection and segmentation. [ABSTRACT FROM AUTHOR]

Manual segmentation of brain tumors using structural magnetic resonance imaging (MRI) is an arduous and time-consuming task. Therefore, automatic and robust segmentation will considerably influence neuro-oncological clinical trials by reducing excessive manual annotation time. Herein, we propose a deep learning model that automatically segments brain tumors even in cases of missing MRI sequences, which are common in practical clinical settings. To address this issue, we enhance a generative adversarial network (GAN) by incorporating a squeeze-and-excitation (SE) attention module into its generator and a PatchGAN into its discriminator. The SE module recalibrates channel responses by explicitly modeling interdependencies, enabling the network to focus on critical regions such as tumor areas. Our proposed generative model is optimized using a combination of adversarial, structural similarity, and mean absolute error losses to synthesize missing MRI sequences more effectively. This enhancement allows our model to synthesize the missing MRI sequence (fluid attenuated inversion recovery [FLAIR]) by leveraging information from other available sequences (T1-weighted, T2-weighted, or contrast-enhanced T1-weighted [T1ce]). For the segmentation task, we employ an optimized nnU-Net model, which is trained using existing sequences and evaluated using both available and synthesized sequences (including missing ones), mimicking real-world scenarios where often only limited MRI sequences are available or usable. Our findings reveal a notable enhancement in brain tumor segmentation, as indicated by a significant increase in overall the Dice similarity coefficient (DSC) from 0.688% (when FLAIR is missing) to 0.873% (when using synthesized FLAIR derived from T2). This improvement brings the segmentation performance closer to what was achieved when real FLAIR was available, where the DSC reaches 0.901%. Moreover, our synthesizing model was also tested on two additional datasets: the BraTS 2020 validation set and BraTS Africa 2023 training set, which produces results comparable to those of BraTS 2021, thereby proving its robustness and generalizability. In addition, the resulting tumor segmentations are subsequently employed to assess the response to treatment in cases where all sequences were available and when synthesis was employed, according to response assessment in neuro-oncology criteria. [Display omitted] [ABSTRACT FROM AUTHOR]

IntroductionThis study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors.MethodsThe MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD).ResultsWhen applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth.DiscussionFusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance.

Abstract Brain tumour segmentation (BTS) is crucial for diagnosis and treatment planning by delineating tumour boundaries and subregions in multi‐modality bio‐imaging data. Several BTS models have been proposed to address specific technical challenges encountered in this field. However, accurately capturing intricate tumour structures and boundaries remains a difficult task. To overcome this challenge, HAB‐Net, a model that combines the strengths of convolutional neural networks and transformer architectures, is presented. HAB‐Net incorporates a custom‐designed hierarchical and pseudo‐convolutional module called hierarchical asymmetric convolutions (HAC). In the encoder, a coordinate attention is included to extract feature maps. Additionally, swin transformer, which has a self‐attention mechanism, is integrated to effectively capture long‐range relationships. Moreover, the decoder is enhanced with a boundary attention module (BAM) to improve boundary information and overall segmentation performance. Extensive evaluations conducted on the BraTS2018 and BraTS2021 datasets demonstrate significant improvements in segmentation accuracy for tumour regions.

Currently, brain tumors are extremely harmful and prevalent. Deep learning technologies, including CNNs, UNet, and Transformer, have been applied in brain tumor segmentation for many years and have achieved some success. However, traditional CNNs and UNet capture insufficient global information, and Transformer cannot provide sufficient local information. Fusing the global information from Transformer with the local information of convolutions is an important step toward improving brain tumor segmentation. We propose the Group Normalization Shuffle and Enhanced Channel Self-Attention Network (GETNet), a network combining the pure Transformer structure with convolution operations based on VT-UNet, which considers both global and local information. The network includes the proposed group normalization shuffle block (GNS) and enhanced channel self-attention block (ECSA). The GNS is used after the VT Encoder Block and before the downsampling block to improve information extraction. An ECSA module is added to the bottleneck layer to utilize the characteristics of the detailed features in the bottom layer effectively. We also conducted experiments on the BraTS2021 dataset to demonstrate the performance of our network. The Dice coefficient (Dice) score results show that the values for the regions of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) were 91.77, 86.03, and 83.64, respectively. The results show that the proposed model achieves state-of-the-art performance compared with more than eleven benchmarks. [ABSTRACT FROM AUTHOR]

The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased because of the diffuse and irregular nature of BTs, along with varying enhancement patterns and the coexistence of different tumor components. Hence, the development of an automated diagnostic system for BTs is vital for mitigating subjective bias and achieving speedy and effective BT segmentation. Recently developed deep learning (DL)-based methods have replaced subjective methods; however, these DL-based methods still have a low performance, showing room for improvement, and are limited to heterogeneous dataset analysis. Herein, we propose a DL-based parallel features aggregation network (PFA-Net) for the robust segmentation of three different regions in a BT scan, and we perform a heterogeneous dataset analysis to validate its generality. The parallel features aggregation (PFA) module exploits the local radiomic contextual spatial features of BTs at low, intermediate, and high levels for different types of tumors and aggregates them in a parallel fashion. To enhance the diagnostic capabilities of the proposed segmentation framework, we introduced the fractal dimension estimation into our system, seamlessly combined as an end-to-end task to gain insights into the complexity and irregularity of structures, thereby characterizing the intricate morphology of BTs. The proposed PFA-Net achieves the Dice scores (DSs) of 87.54%, 93.42%, and 91.02%, for the enhancing tumor region, whole tumor region, and tumor core region, respectively, with the multimodal brain tumor segmentation (BraTS)-2020 open database, surpassing the performance of existing state-of-the-art methods. Additionally, PFA-Net is validated with another open database of brain tumor progression and achieves a DS of 64.58% for heterogeneous dataset analysis, surpassing the performance of existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Brain tumour segmentation (BTS) is crucial for diagnosis and treatment planning by delineating tumour boundaries and subregions in multi‐modality bio‐imaging data. Several BTS models have been proposed to address specific technical challenges encountered in this field. However, accurately capturing intricate tumour structures and boundaries remains a difficult task. To overcome this challenge, HAB‐Net, a model that combines the strengths of convolutional neural networks and transformer architectures, is presented. HAB‐Net incorporates a custom‐designed hierarchical and pseudo‐convolutional module called hierarchical asymmetric convolutions (HAC). In the encoder, a coordinate attention is included to extract feature maps. Additionally, swin transformer, which has a self‐attention mechanism, is integrated to effectively capture long‐range relationships. Moreover, the decoder is enhanced with a boundary attention module (BAM) to improve boundary information and overall segmentation performance. Extensive evaluations conducted on the BraTS2018 and BraTS2021 datasets demonstrate significant improvements in segmentation accuracy for tumour regions. [ABSTRACT FROM AUTHOR]

Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SLiRF module and advanced AAM module. The SLiRF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks. [ABSTRACT FROM AUTHOR]

Medical image processing has been used in medical image analysis for many years and has achieved great success. However, one challenge is that medical image processing algorithms ineffectively utilize multi-modality characteristics to further extract features. To address this issue, we propose SSGNet based on UNet, which comprises a selective multi-scale receptive field (SMRF) module, a selective kernel self-attention (SKSA) module, and a skip connection attention module (SCAM). The SMRF and SKSA modules have the same function but work in different modality groups. SMRF functions in the T1 and T1ce modality groups, while SKSA is implemented in the T2 and FLAIR modality groups. Their main tasks are to reduce the image size by half, further extract fused features within the groups, and prevent information loss during downsampling. The SCAM uses high-level features to guide the selection of low-level features in skip connections. To improve performance, SSGNet also utilizes deep supervision. Multiple experiments were conducted to evaluate the effectiveness of our model on the BraTS2018 dataset. SSGNet achieved Dice coefficient scores for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) of 91.04, 86.64, and 81.11, respectively. The results show that the proposed model achieved state-of-the-art performance compared with more than twelve benchmarks. [ABSTRACT FROM AUTHOR]

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3 + (MSGAM-AUNet3 +), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, a critical challenge in most of the previously reported automated medical diagnostics, is a complex problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. The BRATS 2020 dataset is employed to tackle this issue, an extensively utilized dataset for segmenting brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, feature fusion techniques are implemented to augment the amalgamation of data originating from several modalities, enhancing the accuracy and dependability of tumor segmentation. The Dual Vision Transformer-DSUNET model's performance is evaluated using the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47 % for enhanced tumors, 92.38 % for core tumors, and 90.88 % for edema. The cumulative Dice score for the entirety of the classes is 91.29 %. In addition, the model has a high level of accuracy, roughly 99.93 %, which underscores its durability and efficacy in segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning and monitoring. Traditional methods often encounter challenges due to the complexity and variability of tumor shapes and textures. Consequently, there is a growing need for automated solutions to assist healthcare professionals in segmentation tasks, improving efficiency and reducing workload. This study introduces an innovative method for accurately segmenting brain tumors in MRI images by employing a refined 3D UNet model integrated with a Transformer. The goal is to leverage self-attention mechanisms to enhance segmentation capabilities. The proposed model combines Contextual Transformer (CoT) and Double Attention (DA) architectures. CoT is extended to a 3D format and integrated with the baseline model to exploit intricate contextual details in MRI images. DA blocks in skip connections aggregate and distribute long-range features, emphasizing inter-dependencies within an expanded spatial scope. Experimental results demonstrate superior segmentation performance compared to current state-of-the-art methods. With its ability to accurately segment and delineate tumors in 3D, our segmentation model promises to be a powerful tool for medical image processing and performance optimization, saving time for healthcare professionals and healthcare systems.

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Deep learning technologies have led to substantial breakthroughs in the field of biomedical image analysis. Accurate brain tumor segmentation is an essential aspect of treatment planning. Radiologists agree that manual segmentation is a difficult and time-consuming task that frequently causes delays in the diagnosing process. While U-Net-based methods have been widely used for brain tumor segmentation, many challenges persist, particularly when dealing with tumors of varying sizes, locations, and shapes. Additionally, segmenting tumor regions with structures requires a comprehensive model, which can increase computational complexity and potentially cause gradient vanishing issues. This study presents a novel method called 3D Guided Attention-based deep Inception Residual U-Net (GAIR-U-Net) to address these challenges. This model combines attention mechanisms, an inception module, and residual blocks with dilated convolution to enhance feature representation and spatial context understanding. The backbone of the model is the U-Net model, which leverages the power of inception and residual connections to capture intricate patterns and hierarchical features while expanding the model’s width in three-dimensional space without significantly increasing computational complexity. The attention mechanisms play a role in focusing on important regions and areas while downgrading irrelevant details. The dilated convolutions in the network help in learning both local and global information, improving accuracy and adaptability in segmenting tumors. All the experiments in this study were carried out on multimodal MRI scans that include (T1-weighted, T1-ce, T2-weighted, and FLAIR sequences) from the BraTS 2020 dataset. The presented model is trained and tested on the same dataset, which exhibited promising performance compared to previous methods. On the BraTS 2020 validation dataset, the proposed model obtained a dice score of 0.8796, 0.8634, and 0.8441 for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. These results demonstrate the model’s efficacy in precisely segmenting brain tumors across various modalities. Comparative analyses underscore the model’s versatility in handling tumor shape variations, size, and location, making it a promising solution for clinical applications.

Neurological and brain-related cancers are one of the main causes of death worldwide. A commonly used tool in diagnosing these conditions is Magnetic Resonance Imaging (MRI), yet the manual evaluation of MRI images by medical experts presents difficulties due to time constraints and variability. This research introduces a novel, two-module computerized method aimed at increasing the speed and accuracy of brain tumor detection. The first module, termed the Image Enhancement Technique, utilizes a trio of machine learning and imaging strategies—adaptive Wiener filtering, neural networks, and independent component analysis—to normalize images and combat issues such as noise and varying low region contrast. The second module uses Support Vector Machines to validate the output of the first module and perform tumor segmentation and classification. Applied to various types of brain tumors, including meningiomas and pituitary tumors, our method exhibited significant improvements in contrast and classification efficiency. It achieved an average sensitivity and specificity of 0.991, accuracy of 0.989, and a Dice score (DSC) of 0.981. Furthermore, the processing time of our method, averaging at 0.43 seconds, was markedly lower compared to existing methods. These results underscore the superior performance of our approach over current state-of-the-art methods in terms of sensitivity, specificity, precision, and DSC. Future enhancements will seek to increase the robustness of the tumor classification method by employing a standardized approach across a suite of classifiers.

Precise segmentation is vital for successful diagnosis and treatment planning. Medical image segmentation has demonstrated remarkable advances with the introduction of deep convolutional neural networks, particularly encoder-decoder networks such as U-Net. Despite their excellent performances, these methods have some limitations. First, the structure is limited in its ability to combine information because feature maps to extract valid information from the final encoding stage are incompatible at the encoding and decoding levels. Second, the approach ignores significant semantic details and does not consider different types of small-scale contextual information when segmenting medical images. Lastly, most methods employing 3D architectures to process input medical images increase the computational complexity of the model without significantly improving the accuracy. To resolve these issues, we propose a segmentation network called Multi-Attention Gated Residual U-Net (MAGRes-UNet). This network incorporates four multi-attention gate (MAG) modules and residual blocks into a standard U-Net structure. The MAG module integrates the information from all encoding stages and focuses on small-scale tumors while disambiguating irrelevant and noisy feature responses, thereby promoting meaningful contextual information. The residual blocks simplify the network training and mitigate the problem of vanishing gradients. This improves the ability of the network to effectively learn intricate features and deep representations. Moreover, our network employs the Mish and ReLU activation functions (AFs), which utilize AdamW and Adam optimization strategies to achieve enhanced segmentation performance. The proposed MAGRes-UNet method was compared with the U-Net, Multi-Attention Gated-UNet (MAG-UNet), and Residual-UNet (ResUNet) models. In addition, a statistical T-test was performed to assess the difference in model significance between the approaches. The analysis revealed that MAGRes-UNet employing Mish and AdamW provides significant performance improvement over the ReLU AF and Adam optimizer on two benchmark datasets: Multi-Class BT T1-weighted Contrast-Enhanced Magnetic Resonance Imaging (T1-CE-MRI) and skin lesions HAM10000 (Human Against Machine with 10,000 training images). MAGRes-UNet using Mish and AdamW provides competitive performance over the representative medical image segmentation methods.

The human brain is an incredible and wonderful organ that governs all body actions. Due to its great importance, any defect in the shape of its regions should be reported quickly to reduce the death rate. The abnormal region segmentation helps to plan and monitor the treatment. The most critical procedure is isolating normal and abnormal tissues from each other. So far, remarkable imaging modalities are being used to diagnose abnormalities at their early stages, and magnetic resonance imaging (MRI) is renowned and noninvasive among those modalities. This paper investigates the current landscape of brain tumor segmentation (BTS) by exploring emerging deep learning (DL) methods for brain MRI analysis. The findings offer a comprehensive comparison of recent DL approaches, emphasizing their effectiveness in handling diverse tumor types while addressing limitations associated with data scarcity and robust validation. DL has shown a vital improvement for BTS, so our primary focus is to include significant DL robust models to analyze the brain MRI. However, DL outperforms traditional methods; still, there are several limitations, especially related to the diverse tumor types, lack of datasets, and weak validations. The future perspectives of DL-based BTS present significant potential for revolutionizing the diagnosis and treatment of brain tumors.

Medical image segmentation is a challenging and popular task in the field of medical image processing in recent decades. Most of the current mainstream segmentation networks are based on convolutional neural networks (CNNs) methods. Among them, encoding and decoding structures based on U-Net architecture and skip connection mechanism have made great progress in medical segmentation. However, these networks come with increased complexity and training difficulty as the accuracy of network segmentation continues to increase, and their ability remains to be improved for extracting feature information in specific information-intensive structure segmentation tasks, such as brain tumors. In addition, the high training cost raises the application threshold of medical image segmentation. To address these issues, we introduce a frequency representation approach that can effectively reduce the loss of feature during encoding and decoding of segmentation networks. Then a tokenized multi-layer perceptron (MLP) method is introduced to learn the space information. Frequency representation and tokenized MLP can greatly reduce the parameters and computational effort while achieving more accurate and efficient medical image segmentation. Therefore, a multi-level lightweight U-Net segmentation network named MLU-Net is proposed to perform segmentation tasks of medical images quickly. In brain tumor segmentation experiments under equivalent preprocessing conditions, our network achieves substantial efficiency gains with parameter and computational workload reductions to 1/39 and 1/61 of U-Net’s, while simultaneously demonstrating superior performance, enhancing the Dice and Intersection over Union (IoU) metrics by 3.37% and 3.30%, respectively. In addition, we perform experiments on dermatologic data and still achieve segmentation performance that outperforms comparable networks. These experiments show that the proposed network is characterized by lightweight and high accuracy, which is contributing to the exploration of clinical medicine scenarios.

In order to improve the segmentation effect of brain tumor images and address the issue of feature information loss during convolutional neural network (CNN) training, we present an MRI brain tumor segmentation method that leverages an enhanced U-Net architecture. First, the ResNet50 network was used as the backbone network of the improved U-Net, the deeper CNN can improve the feature extraction effect. Next, the Residual Module was enhanced by incorporating the Convolutional Block Attention Module (CBAM). To increase characterization capabilities, focus on important features and suppress unnecessary features. Finally, the cross-entropy loss function and the Dice similarity coefficient are mixed to compose the loss function of the network. To solve the class unbalance problem of the data and enhance the tumor area segmentation outcome. The method's segmentation performance was evaluated using the test set. In this test set, the enhanced U-Net achieved an average Intersection over Union (IoU) of 86.64% and a Dice evaluation score of 87.47%. These values were 3.13% and 2.06% higher, respectively, compared to the original U-Net and R-Unet models. Consequently, the proposed enhanced U-Net in this study significantly improves the brain tumor segmentation efficacy, offering valuable technical support for MRI diagnosis and treatment.

Brain tumor segmentation using neural networks presents challenges in accurately capturing diverse tumor shapes and sizes while maintaining real-time performance. Additionally, addressing class imbalance is crucial for achieving accurate clinical results. To tackle these issues, this study proposes a novel N-shaped lightweight network that combines multiple feature pyramid paths and U-Net architectures. Furthermore, we ingeniously integrate hybrid attention mechanisms into various locations of depth-wise separable convolution module to improve efficiency, with channel attention found to be the most effective for skip connections in the proposed network. Moreover, we introduce a combination loss function that incorporates a newly designed weighted cross-entropy loss and dice loss to effectively tackle the issue of class imbalance. Extensive experiments are conducted on four publicly available datasets, i.e., UCSF-PDGM, BraTS 2021, BraTS 2019, and MSD Task 01 to evaluate the performance of different methods. The results demonstrate that the proposed network achieves superior segmentation accuracy compared to state-of-the-art methods. The proposed network not only improves the overall segmentation performance but also provides a favorable computational efficiency, making it a promising approach for clinical applications. [ABSTRACT FROM AUTHOR]

Brain tumors were the most common kind of tumor in humans. Brain tumors can be detected from various imaging technologies. The proposed research work strives to improve the prediction accuracy of brain tumor detection and segmentation from MRI of human head scans by using a novel activation function E-Tanh. The role of activation functions is to perform computations and make decisions in artificial neural networks (ANN). We developed three ANN models for brain tumor detection by modifying the hidden layers. We have trained these ANN models using the E-Tanh activation function and evaluated their performance. This novel activation function achieved 98% prediction accuracy for the MRI brain tumor image detection neural network model, which was higher than the existing activation functions. We also have segmented brain tumors from the BraTS2020 dataset by using this activation function in U-Net-based architecture. We attained dice scores of 83%, 95%, and 85% for the whole, core, and enhancing tumors, which are significantly higher than the ReLU activation function. [ABSTRACT FROM AUTHOR]

Introduction: This study presents a novel continuous learning framework tailored for brain tumour segmentation, addressing a critical step in both diagnosis and treatment planning. This framework addresses common challenges in brain tumour segmentation, such as computational complexity, limited generalisability, and the extensive need for manual annotation. Methods: Our approach uniquely combines multi-scale spatial distillation with pseudo-labelling strategies, exploiting the coordinated capabilities of the ResNet18 and DeepLabV3+ network architectures. This integration enhances feature extraction and efficiently manages model size, promoting accurate and fast segmentation. To mitigate the problem of catastrophic forgetting during model training, our methodology incorporates a multi-scale spatial distillation scheme. This scheme is essential for maintaining model diversity and preserving knowledge from previous training phases. In addition, a confidence-based pseudo-labelling technique is employed, allowing the model to self-improve based on its predictions and ensuring a balanced treatment of data categories. Results: The effectiveness of our framework has been evaluated on three publicly available datasets (BraTS2019, BraTS2020, BraTS2021) and one proprietary dataset (BraTS_FAHZU) using performance metrics such as Dice coefficient, sensitivity, specificity and Hausdorff95 distance. The results consistently show competitive performance against other state-of-the-art segmentation techniques, demonstrating improved accuracy and efficiency. Discussion: This advance has significant implications for the field ofmedical image segmentation. Our code is freely available at https://github.com/smallboy-code/A-brain-tumor-segmentation-frameworkusing-continual-learning. [ABSTRACT FROM AUTHOR]

Brain tumor image segmentation plays a significant auxiliary role in clinical diagnosis. Recently, deep learning has been introduced into multimodal segmentation tasks, which construct various Convolutional Neural Network (CNN) structures to achieve excellent performance. However, most CNN-based segmentation methods have poor capability for global feature extraction. Transformer is good at modeling long-distance dependencies, but it can cause local information loss and usually has a high computational complexity. In addition, it is difficult to fully exploit the brain tumor features of different modalities. To address these issues, in this paper, we propose a region–attention fusion (RAF) network that combines a dual-branch vision Transformer (DVT), called RFTNet. In RFTNet, the DVT is exploited to capture the delicate local information and global semantics separately by two branches. Meanwhile, a novel RAF is employed to effectively fuse the images of the different modalities. Finally, we design a new hybrid loss function, called region-mixed loss function (RML) to calculate the importance of each pixel and solve the problem of class imbalance. The experiments on BrasTS2018 and BraTS2020 datasets show that our method obtains a higher segmentation accuracy than other models. Furthermore, ablation experiments prove the effectiveness of each key component in RFTNet. [ABSTRACT FROM AUTHOR]

Computer-aided brain tumor segmentation using magnetic resonance imaging (MRI) is of great significance for the clinical diagnosis and treatment of patients. Recently, U-Net has received widespread attention as a milestone in automatic brain tumor segmentation. Following its merits and motivated by the success of the attention mechanism, this work proposed a novel mixed attention U-Net model, i.e., MAU-Net, which integrated the spatial-channel attention and self-attention into a single U-Net architecture for MRI brain tumor segmentation. Specifically, MAU-Net embeds Shuffle Attention using spatial-channel attention after each convolutional block in the encoder stage to enhance local details of brain tumor images. Meanwhile, considering the superior capability of self-attention in modeling long-distance dependencies, an enhanced Transformer module is introduced at the bottleneck to improve the interactive learning ability of global information of brain tumor images. MAU-Net achieves enhancing tumor, whole tumor and tumor core segmentation Dice values of 77.88/77.47, 90.15/90.00 and 81.09/81.63% on the brain tumor segmentation (BraTS) 2019/2020 validation datasets, and it outperforms the baseline by 1.15 and 0.93% on average, respectively. Besides, MAU-Net also demonstrates good competitiveness compared with representative methods.

Abstract Purpose Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. Materials and methods Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. Results Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors.

The escalating incidence of accurate detection of brain tumors within the discipline of neuro-oncology underscores the pressing demand for enhanced diagnostic methodologies. The extant corpus of literature, predominantly focused on the categorization of MRI images, is deficient in comprehensive solutions for the myriad challenges faced in the segmentation of brain tumors, including imaging anomalies, the nuanced delineation of tumor margins, tumor heterogeneity, and classification ambiguities. The aim of this research endeavor is to address these challenges by proposing a novel deep learning framework that integrates the renowned U-Net architecture with self-attention mechanisms, meticulously tailored for the segmentation of brain tumors.Our study rigorously evaluates and contrasts prevailing deep learning techniques, with an emphasis on the efficacy of the U-Net architecture in discerning both specific and generalized features within three-dimensional brain imaging. The integration of self-attention mechanisms is demonstrably effective in augmenting segmentation accuracy by directing focus towards pivotal tumor regions and enhancing overall precision. Principal findings reveal that our proposed model surpasses recent developments in brain tumor segmentation from the years 2020–2024 in metrics of accuracy, precision, sensitivity, and specificity. Significant conclusions indicate that this amalgamation establishes a novel benchmark in medical image segmentation, possessing the capacity to revolutionize diagnostic capabilities and therapeutic strategies. The ramifications extend beyond academic discourse, offering a glimmer of optimism for patients and healthcare professionals alike in the precise diagnosis and management of brain tumors.

Glioma is the most prevalent primary malignant brain tumor. Segmentation of glioma regions using magnetic resonance imaging (MRI) is essential for treatment planning. However, segmentation of glioma regions is usually based on four MRI modalities, which are T1, T2, T1Gd, and FLAIR. Acquiring these four modalities will increase patients' time inside the scanner and drive up the segmentation process's processing time. Nevertheless, not all these modalities are acquired in some cases due to the limited available time on the MRI scanner or uncooperative patients. Therefore, U-Net-based fully convolutional neural networks were employed for automated segmentation to answer the urgent question: does a smaller number of MRI modalities limit the segmentation accuracy? The proposed approach was trained, validated, and tested on 100 high-grade glioma (HGG) cases twice, once with all MRI sequences and second with only FLAIR and T1Gd. The results on the test set showed that the baseline U-Net model gave a mean Dice score of 0.9166 and 0.9190 on all MRI sequences using FLAIR and T1Gd, respectively. To check for possible performance improvement of the U-Net on FLAIR and T1Gd modalities, an ensemble of pre-trained VGG16, VGG19, and ResNet50 as modified U-Net encoders were employed for automated glioma segmentation based on T1Gd and FLAIR modalities only and compared with the baseline U-Net. The proposed models were trained, validated, and tested on 259 high-grade gliomas (HGG) cases. The results showed that the proposed baseline U-Net model and the ensemble of pre-trained VGG16, VGG19, or ResNet50 as modified U-Net encoders have a mean Dice score of 0.9395, 0.9360, 0.9359, and 0.9356, respectively. The results were also compared to other studies based on four MRI modalities. The work indicates that FLAIR and T1Gd are the most prominent contributors to the segmentation process. The proposed baseline U-Net is robust enough for segmenting HGG sub-tumoral structures and competitive with other state-of-the-art works.

The Augmented Transformer U-Net (AugTransU-Net) is proposed to address limitations in existing transformer-related U-Net models for brain tumor segmentation. While previous models effectively capture long-range dependencies and global context, these works ignore the hierarchy to a certain degree and need more feature diversity as depth increases. The proposed AugTransU-Net integrates two advanced transformer modules into different positions within a U-shaped architecture to overcome these issues. The fundamental innovation lies in constructing improved augmentation transformer modules that incorporate Augmented Shortcuts into standard transformer blocks. These augmented modules are strategically placed at the bottleneck of the segmentation network, forming multi-head self-attention blocks and circulant projections, aiming to maintain feature diversity and enhance feature interaction and diversity. Furthermore, paired attention modules operate from low to high layers throughout the network, establishing long-range relationships in both spatial and channel dimensions. This allows each layer to comprehend the overall brain tumor structure and capture semantic information at critical locations. Experimental results demonstrate the effectiveness and competitiveness of AugTransU-Net in comparison to representative works. The model achieves Dice values of 89.7%/89.8%, 78.2%/78.6%, and 80.4%/81.9% for whole tumor (WT), enhancing tumor (ET) and tumor core (TC) segmentation on the BraTS2019-2020 validation datasets, respectively. The code for AugTransU-Net will be made publicly available at https://github.com/MuqinZ/AugTransUnet.

Simple Summary: In our study, we addressed the challenge of the brain tumor segmentation task using a range of MRI modalities. While leading models show proficiency on standardized datasets, their versatility across different clinical environments remains uncertain. We introduced 'Region-Focused Selection Plus (RFS+)', enhancing the segmentation performance for clinically defined labels like gross tumor volume in our local dataset. RFS+ integrates segmentation approaches and normalization techniques, leveraging the strengths of each approach and minimizing their drawbacks by selecting the top three models. RFS+ demonstrated efficient brain tumor segmentation, using 67% less memory and requiring 92% less training time than the state-of-the-art model. The strategy achieved better performance compared to the leading model, with a 79.22% dice score. These findings highlight the potential of RFS+ in amplifying the adaptability of deep learning models for brain tumor segmentation in clinical applications. However, further research is needed to validate the broader clinical efficacy of RFS+. Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and echo time) remains a subject of debate. This research aims to address this gap by introducing a novel 'Region-Focused Selection Plus (RFS+)' strategy designed to efficiently improve the generalization and quantification capabilities of deep learning (DL) models for automatic brain tumor segmentation. RFS+ advocates a targeted approach, focusing on one region at a time. It presents a holistic strategy that maximizes the benefits of various segmentation methods by customizing input masks, activation functions, loss functions, and normalization techniques. Upon identifying the top three models for each specific region in the training dataset, RFS+ employs a weighted ensemble learning technique to mitigate the limitations inherent in each segmentation approach. In this study, we explore three distinct approaches, namely, multi-class, multi-label, and binary class for brain tumor segmentation, coupled with various normalization techniques applied to individual sub-regions. The combination of different approaches with diverse normalization techniques is also investigated. A comparative analysis is conducted among three U-net model variants, including the state-of-the-art models that emerged victorious in the BraTS 2020 and 2021 challenges. These models are evaluated using the dice similarity coefficient (DSC) score on the 2021 BraTS validation dataset. The 2D U-net model yielded DSC scores of 77.45%, 82.14%, and 90.82% for enhancing tumor (ET), tumor core (TC), and the whole tumor (WT), respectively. Furthermore, on our local dataset, the 2D U-net model augmented with the RFS+ strategy demonstrates superior performance compared to the state-of-the-art model, achieving the highest DSC score of 79.22% for gross tumor volume (GTV). The model utilizing RFS+ requires 10% less training dataset, 67% less memory and completes training in 92% less time compared to the state-of-the-art model. These results confirm the effectiveness of the RFS+ strategy for enhancing the generalizability of DL models in brain tumor segmentation. [ABSTRACT FROM AUTHOR]

Cluster computing is an essential technology in distributed environments for practical data analysis in complex datasets like tumor segmentation, disease classification etc. Today real-world applications like medicine and transport are needed for big data analytics environments. This research article considers complex image data environments like brain tumor segmentation based on advanced clustering techniques for effective tumor prediction. An a-state-of-art analysis used Hierarchical clustering to extract initial tumor segments from the image. The next segment is further refined using novel Noise detection-based levelsetting techniques. The unsupervised Fuzzy C-means and k-means clustering is used to segment the diseases affected region to enhance noise detection used in the level set. Effective features are extracted using gray level co-occurrence matrix and redundant discrete wavelet transform. Finally, classifying malignant and benign brain tumor images is done using deep probabilistic neural networks. Publicly available datasets are used to validate the proposed algorithms. Experimental results prove that proposed pipeline techniques have effective performance in tumor segmentation and classification model. [ABSTRACT FROM AUTHOR]

Purpose: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. Materials and methods: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. Results: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences. [ABSTRACT FROM AUTHOR]

Accurate segmentation of brain tumors from magnetic resonance images is the key to the clinical diagnosis and rational treatment of brain tumor diseases. Recently, convolutional neural networks have been widely used in biomedical image processing. 3D U-Net is sought after because of its excellent segmentation effect; however, the feature map supplemented by the skip connection is the output feature map after the encoder feature extraction, and the loss of original detail information in this process is ignored. In the 3D U-Net design, after each layer of convolution, regularization, and activation function processing, the detailed information contained in the feature map will deviate from the original detailed information. For skip connections, the essence of this design is to supplement the detailed information of the original features to the decoder; that is, in the decoder stage, the more original the skip connection-supplemented feature maps are, the more easily the decoder can achieve a better segmentation effect. To address this problem, this paper proposes the concept of a front-skip connection. That is, the starting point of the skip connection is adjusted to the front to improve the network performance. On the basis of this idea, we design a front-skip connection inverted residual U-shaped network (FS Inv-Res U-Net). First, the front-skip connections are applied to three typical networks, DMF Net, HDC Net, and 3D U-Net, to verify their effectiveness and generalization. Applying our proposed front-skip connection concept on these three networks improves the network performance, indicating that the idea of a front-skip connection is simple but powerful and has out-of-the-box characteristics. Second, 3D U-Net is enhanced using the front-skip connection concept and the inverted residual structure of MobileNet, and then FS Inv-Res U-Net is proposed based on these two ideas. Additionally, ablation experiments are conducted on FS Inv-Res U-Net. After adding the front-skip connection and the inverted residual module to the backbone network 3D U-Net, the segmentation performance of the proposed network is greatly improved, indicating that the front-skip connection and the inverted residual module help our brain tumor segmentation network. Finally, the proposed network is validated on the validation dataset of the public datasets BraTS 2018 and BraTS 2019. The Dice scores of the validation results on the enhanced tumor, whole tumor, and tumor core were 80.23%, 90.30%, and 85.45% and 78.38%, 89.78%, and 83.01%, respectively; the hausdorff95 distances were 2.35, 4.77, and 5.50 mm and 4, 5.57, and 6.37 mm, respectively. The above results show that the FS Inv-Res U-Net proposed in this paper achieves the same evaluation indicators as advanced networks and provides accurate brain tumor segmentations.

Currently, brain tumors are extremely harmful and prevalent. Deep learning technologies, including CNNs, UNet, and Transformer, have been applied in brain tumor segmentation for many years and have achieved some success. However, traditional CNNs and UNet capture insufficient global information, and Transformer cannot provide sufficient local information. Fusing the global information from Transformer with the local information of convolutions is an important step toward improving brain tumor segmentation. We propose the Group Normalization Shuffle and Enhanced Channel Self-Attention Network (GETNet), a network combining the pure Transformer structure with convolution operations based on VT-UNet, which considers both global and local information. The network includes the proposed group normalization shuffle block (GNS) and enhanced channel self-attention block (ECSA). The GNS is used after the VT Encoder Block and before the downsampling block to improve information extraction. An ECSA module is added to the bottleneck layer to utilize the characteristics of the detailed features in the bottom layer effectively. We also conducted experiments on the BraTS2021 dataset to demonstrate the performance of our network. The Dice coefficient (Dice) score results show that the values for the regions of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) were 91.77, 86.03, and 83.64, respectively. The results show that the proposed model achieves state-of-the-art performance compared with more than eleven benchmarks.