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Evaluation of 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) magnetic resonance imaging (MRI) provides excellent bone contrast and can potentially be used in place of computed tomography. Segmentation of shoulder anatomy, particularly humeral head and acetabulum, is needed for detailed assessment of each anatomy and for pre-surgical preparation. In this study we compared performance of two popular deep learning models based on Google's DeepLab and U-Net to perform automated segmentation on ZTE MRI of human shoulders. Axial ZTE images of normal shoulders (n=31) acquired at 3-Tesla were annotated for training with a DeepLab and 2D U-Net, and the trained model was validated with testing data (n=13). While both models showed visually satisfactory results for segmenting the humeral bone, U-Net slightly over-estimated while DeepLab under-estimated the segmented area compared to the ground truth. Testing accuracy quantified by Dice score was significantly higher (p

For non-destructive evaluation, the segmentation of infrared thermographic images of carbon fibre composites is a critical task in material characterisation and quality assessment. This paper presents a study on the application of image processing techniques, particularly adaptive thresholding, and advanced neural network models, including U-Net, DeepLabv3, and BiLSTM, for the segmentation of infrared images. This work introduces the innovative combination of DeepLabv3 and BiLSTM applied in infrared images of carbon fibre-reinforced polymer samples for the first time, proposing it as a novel approach for enhancing the accuracy of segmentation tasks. An experimental comparison of these models was conducted to assess their effectiveness in identifying artificial defects in these images. The performance of each model was evaluated using the F1-Score and Intersection over Union (IoU) metrics. The results demonstrate that the proposed combination of DeepLabv3 and BiLSTM outperforms other methods, achieving an F1-Score of 0.96 and an IoU of 0.83, showcasing its potential for advanced material analysis and quality control. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is an extensively explored vibrational spectroscopic technique to analyze the biochemical composition and molecular structure of samples, which is often assumed to be non-destructive when carefully using proper laser power and exposure time. However, the inherently weak Raman signal and concurrent fluorescence interference often lead to Raman measurements with a low signal-to-noise ratio (SNR), especially for biological samples. Great efforts have been made to develop experimental approaches and/or numerical algorithms to improve the SNR. In this study, we proposed an ensemble learning approach to recover and denoise Raman measurements with a low SNR. The proposed ensemble learning approach was evaluated on 986 pairs of Raman measurements, each pair of which consists of a low SNR Raman spectrum and a high SNR reference Raman spectrum from the exact same fungal sample but uses 200 times the integration time. Compared with conventional methods, the Raman measurements recovered by the proposed ensemble learning approach are more identical to high SNR reference Raman measurements, with an average RMSE and MAE of only 1.337 × 10−2 and 1.066 × 10−2, respectively; thus, the proposed ensemble learning approach is expected to be a powerful tool for numerically improving the SNR of Raman measurements and further benefits rapid Raman acquisition from biological samples. [ABSTRACT FROM AUTHOR]

U-Net and its variants have been widely used in the field of image segmentation. In this paper, a lightweight multi-scale Ghost U-Net (MSGU-Net) network architecture is proposed. This can efficiently and quickly process image segmentation tasks while generating high-quality object masks for each object. The pyramid structure (SPP-Inception) module and ghost module are seamlessly integrated in a lightweight manner. Equipped with an efficient local attention (ELA) mechanism and an attention gate mechanism, they are designed to accurately identify the region of interest (ROI). The SPP-Inception module and ghost module work in tandem to effectively merge multi-scale information derived from low-level features, high-level features, and decoder masks at each stage. Comparative experiments were conducted between the proposed MSGU-Net and state-of-the-art networks on the ISIC2017 and ISIC2018 datasets. In short, compared to the baseline U-Net, our model achieves superior segmentation performance while reducing parameter and computation costs by 96.08 and 92.59%, respectively. Moreover, MSGU-Net can serve as a lightweight deep neural network suitable for deployment across a range of intelligent devices and mobile platforms, offering considerable potential for widespread adoption. [ABSTRACT FROM AUTHOR]

Image hiding aims to secretly embed secret information into a cover image and then recover the hidden data with minimal or no loss at the receiving end. Many works on steganography and deep learning have proved the huge prospects of deep learning in the field of image information hiding. However, current deep learning-based steganography research exposes significant limits, among which key issues such as how to improve embedding capacity, imperceptibility, and robustness remain crucial for image-hiding tasks. This article introduces PUA-Net, a new end-to-end neural network model for image steganography. PUA-Net consists of three main components: 1) the CbDw attention module, 2) the attention gate module, and 3) the partial combination convolution module. Each of these components utilizes structural reparameterization operations. In addition, we propose a residual image minimization loss function and use a combination of loss functions based on this loss function. This model can seamlessly embed bit stream information of different capacities into images to generate stego images that are imperceptible to the human eye. Experimental results confirm the effectiveness of our model, achieving an RS-BPP of 5.98 when decoding the extracted secret information and recovering the cover image. When only the extracted secret information is output, the model achieves a maximum RS-BPP of 6.94. Finally, experimental results show that our PUA-Net model outperforms deep learning-based steganography approaches on COCO, ImageNet, and BOSSbase datasets, including GAN-based methods such as Stegastamp and SteganoGAN. [ABSTRACT FROM AUTHOR]

Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively. Yet, this design typically falls short in leveraging shallow structural information to enrich the dual branches with comprehensive multiscale data. Additionally, the lightweight components struggle to capture the global contextual details of feature sets efficiently. When compared to state-of-the-art models, lightweight semantic segmentation models usually exhibit performance gaps. To address these issues, we introduce a novel approach that incorporates a deep-shallow interaction mechanism with an attention module to improve water body segmentation efficiency. This method spatially adjusts feature representations to better identify water-related data, utilizing a U-Net frame work to enhance the accuracy of edge detection in water zones by providing more precise local positioning information. The attention mechanism processes and merges low and high-level data separately in different dimensions, allowing for the effective distinction of water areas from their surroundings by blending spatial attributes with in-depth context insights. Experimental outcomes demonstrate a remarkable 95% accuracy, showcasing the proposed method's superiority over existing models. [ABSTRACT FROM AUTHOR]

Image dehazing is an ill-posed issue in low-level computer vision; therefore, it grabbed many researchers' attention. The key mechanism to improve dehazing performance remains unclear, although many existing network pipelines work fine. To improve the performance of the image dehazing network, a hierarchical model named "Selective Attentive Wavelet M-Net+" (SWAM-Net+) was proposed. In order to enrich the features from the wavelet domain, a "Selective Wavelet Attentive Module" was introduced in M-Net+. Several key components of our network are used for extracting the multiscale features through parallel multi-resolution convolution channels. Contextual information is collected using a dual attention unit, and the attention is based on multiscale feature aggregation. We replaced summation and concatenation operations by introducing the Selective Kernel Feature Fusing module to achieve feature aggregation. Furthermore, our network achieves comprehensively better performance results on the RESIDE dataset both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism's development. Although current testing primarily relies on large mammalian models, the emergence of new approach methodologies (NAMs) is encouraging industries and regulatory agencies to evaluate novel assays. C. elegans have emerged as NAMs for rapid toxicity testing because of its biological relevance and suitability to high throughput studies. However, current low-resolution and labor-intensive methodologies prohibit its application for sub-lethal DevTox studies at high throughputs. With the recent advent of the large-scale microfluidic device, vivoChip, we can now rapidly collect 3D high-resolution images of ~ 1000 C. elegans from 24 different populations. While data collection is rapid, analyzing thousands of images remains time-consuming. To address this challenge, we developed a machine-learning (ML)-based image analysis platform using a 2.5D U-Net architecture (vivoBodySeg) that accurately segments C. elegans in images obtained from vivoChip devices, achieving a Dice score of 97.80%. vivoBodySeg processes 36 GB data per device, phenotyping multiple body parameters within 35 min on a desktop PC. This analysis is ~ 140 × faster than the manual analysis. This ML approach delivers highly reproducible DevTox parameters (4–8% CV) to assess the toxicity of chemicals with high statistical power. [ABSTRACT FROM AUTHOR]

Individual differences in organ size and spatial distribution can lead to significant variations in the content of medical images at similar anatomical locations. These case-level differences are distinct from the domain shift between multi-source data, yet they can significantly affect model performance and are difficult to address using traditional transfer learning algorithms such as domain generalization. To address the individual difference issue, we propose an individual difference aware meta-learning strategy and introduce an individual discriminator module. These components are designed to learn features related to individual difference, enhancing the model's ability to accurately segment organs across different patients. Additionally, we present a Transformer-based U-Net framework that captures both long- and short-range dependencies from MR images. This framework utilizes a parallel attention module to address the limitations of self-attention and employs an inter-layer attention module to extract attention relationships across different layers. We evaluate our approach using the Synapse dataset. Results indicate that focusing on individual difference not only significantly improves the performance of various sub-modules, allowing our method to surpass several state-of-the-art methods, but also proves to be beneficial for many other methods as well. • A hybrid attention network is proposed to better model multiscale relationships. • A meta-learning strategy is designed to achieve better segmentation generalization. • An individual discriminator is designed to extract individual-difference features. [ABSTRACT FROM AUTHOR]

• Pioneering cGAN-based technique revolutionizes MRI tumor segmentation accuracy. • Patch discriminator integration crafts ultra-realistic synthetic MRI datasets. • Attention-augmented U-Net model dramatically boosts feature-focused segmentation. • Synthetic data innovation bridges the gap of limited annotated medical imagery. • Achieves unprecedented precision in brain, liver, and pelvic MRI segmentation tasks. Accurate tumor segmentation within MRI images is of great importance for both diagnosis and treatment; however, in many cases, sufficient annotated datasets may not be available. This paper develops a novel approach to the medical image segmentation of tumors in the brain, liver, and pelvic regions within MRI images, by combining an attention-enhanced U-Net model with a cGAN. We introduce three key novelties: a patch discriminator in the cGAN to enhance realism of generated images, attention mechanisms in the U-Net to enhance the accuracy of segmentation, and finally an application to pelvic MRI segmentation, which has seen little exploration. Our method addresses the issue of limited availability of annotated data by generating realistic synthetic images to augment the process of training. Our experimental results on brain, liver, and pelvic MRI datasets show that our approach outperforms the state-of-the-art methods with a Dice Coefficient of 98.61 % for brain MRI, 88.60 % for liver MRI, and 91.93 % for pelvic MRI. We can also observe great increases in the Hausdorff Distance, at especially complex anatomical regions such as tumor boundaries. The proposed combination of synthetic data creation and novel segmentation techniques opens new perspectives for robust medical image segmentation. [ABSTRACT FROM AUTHOR]

Deformable image registration minimizes the discrepancy between moving and fixed images by establishing linear and nonlinear spatial correspondences. It plays a crucial role in surgical navigation, image fusion and disease analysis. Its challenge lies in the large number of deformed parameters and the uncertainty of acquisition conditions. Benefiting from the powerful ability to capture hierarchical features and spatial relationships of convolutional neural networks, the medical image registration task has made great progress. Nowadays, the long-range relationship modeling and adaptive selection of self-attention show great potential and have also attracted much attention from researchers. Inspired by this, we propose a new method called Multi-scale Large Kernel Attention UNet (MLKA-Net), which combines a large kernel convolution with the attention mechanism using a multi-scale strategy, and uses a correction module to fine-tune the deformation field to achieve high-accuracy registration. Specifically, we first propose a multi-scale large kernel attention mechanism (MLKA), which generates attention maps by aggregating information from convolution kernels at different scales to improve local feature modeling capabilities of attention. Furthermore, we employ large kernel dilation convolution in proposed attention to construct sufficiently long-range relationships, while keeping lower number of parameters. Finally, to further improve local accuracy of the registration, we design an additional correction module and unsupervised framework to fine-tune the deformation field to solve the issue of original information loss in multilayer networks. Our method is compared qualitatively and quantitatively with 24 representative and advanced methods on the 3 public available 3D datasets from IXI database, LPBA40 dataset and OASIS database, respectively. The experiments demonstrate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Developmental hip dysplasia (DDH) is a disease in which the hip joint fails to develop normally due to various causes before, during or after birth. The most important method used for the detection of DDH is hip ultrasonography. The stage of obtaining the hip US image varies because it depends on the operator and external influences. In this study, an artificial intelligence-based system has been developed to eliminate this variability. The developed system includes a 2-stage deep learning model. The main purpose of the system is to automatically determine whether the US images obtained by physicians are suitable for the calculation of alpha and beta angles required for diagnosis. The system uses the U-NET architecture in the first stage and the masked region-based convolutional neural network (MBT-ESA) architecture in the second stage. For the training, 540 images were taken from Selçuk University Faculty of Medicine hospital with the approval of the ethics committee. A total of 840 images were obtained for training with data augmentation. U-NET architecture training resulted in an accuracy of 0.93 and region-based convolutional neural network training with mask resulted in an accuracy of 0.96. The overall system accuracy was calculated as 0.96. The results obtained in this study suggest that by increasing the number of real-time tests and images, the inter-operator variability in the diagnosis of DDH can be eliminated. [ABSTRACT FROM AUTHOR]

The U-Net architecture, which performs segmentation on images, has also achieved very successful results in the medical field. However, there is also a need to improve the U-Net architecture for better results. In this article, some improvement proposals are presented for the U-Net model's encoder part, and the segmentation success of the implemented architecture for segmentation of dermoscopic image lesions is evaluated. The PH2 dataset and the "International Skin Imaging Collaboration" datasets (ISIC-2016 and ISIC-2017) were used for the research. The traditional data augmentation method was applied to the selected PH2 dataset samples. The results of the proposed model (EnecaU-Net) and the U-Net model obtained with the PH2 dataset were compared. Furthermore, in this article, the mix data augmentation method, which has an influence on the model's segmentation success, is examined for lesion segmentation on dermoscopic images. This investigation was made with the ISIC-2016 dataset, and its experimental results were compared with the same amount of the ISIC-2017 dataset that didn't apply data augmentation operations. Although, during the evaluation phase, Dice and Jaccard (IoU) metrics were used primarily to measure the success of the model, specificity, sensitivity, and accuracy criteria were also used. According to our results, the segmentation success of the EnecaU-Net model applied for lesion segmentation on dermoscopic images is high, and the applied mix data augmentation method improves the segmentation success of the EnecaU-Net model. The average test results achieved by the proposed model are 88.05% and 80.30% for ISIC2016 and 83.09% and 74.54% for ISIC-2017 in terms of the Dice and Jaccard values, respectively. [ABSTRACT FROM AUTHOR]

Background and Objectives: The increasing prevalence of skin cancer highlights the urgency for early intervention, emphasizing the need for advanced diagnostic tools. Computer-assisted diagnosis (CAD) offers a promising avenue to streamline skin cancer screening and alleviate associated costs. Methods: This study endeavors to develop an automatic segmentation system employing deep neural networks, seamlessly integrating data manipulation into the learning process. Utilizing an encoder-decoder architecture rooted in U-Net and augmented by wavelet transform, our methodology facilitates the generation of high-resolution feature maps, thus bolstering the precision of the deep learning model. Results: Performance evaluation metrics including sensitivity, accuracy, dice coefficient, and Jaccard similarity confirm the superior efficacy of our model compared to conventional methodologies. The results showed a accuracy of %96.89 for skin lesions in PH2 Database and %95.8 accuracy for ISIC 2017 database findings, which offers promising results compared to the results of other studies. Additionally, this research shows significant improvements in three metrics: sensitivity, Dice, and Jaccard. For the PH database, the values are 96, 96.40, and 95.40, respectively. For the ISIC database, the values are 92.85, 96.32, and 95.24, respectively. Conclusion: In image processing and analysis, numerous solutions have emerged to aid dermatologists in their diagnostic endeavors The proposed algorithm was evaluated using two PH datasets, and the results were compared to recent studies. Impressively, the proposed algorithm demonstrated superior performance in terms of accuracy, sensitivity, Dice coefficient, and Jaccard Similarity scores when evaluated on the same database images compared to other methods. [ABSTRACT FROM AUTHOR]

Retinal image segmentation is crucial for the early diagnosis of some diseases like diabetes and hypertension. Current methods face many challenges, such as inadequate multi-scale semantics and insufficient global information. In view of this, we propose a network called multi-scale semantics mining and tiny details enhancement (MSM-TDE). First, a multi-scale feature input module is designed to capture multi-scale semantics information from the source. Then a fresh multi-scale attention guidance module is constructed to mine local multi-scale semantics while a global semantics enhancement module is proposed to extract global multi-scale semantics. Additionally, an auxiliary vessel detail enhancement branch using dynamic snake convolution is built to enhance the tiny vessel details. Extensive experimental results on four public datasets validate the superiority of MSM-TDE, which obtains competitive performance with satisfactory model complexity. Notably, this study provides an innovative idea of multi-scale semantics mining by diverse methods. [ABSTRACT FROM AUTHOR]

The examination of power transmission lines using UAVs (Unmanned Aerial Vehicles) is crucial for ensuring grid security. However, it is challenging for existing deep learning models to achieve a balance between accuracy and efficiency in recognizing power lines, especially when they are affected by intricate environmental backdrops and the thin structure of power lines. To address this issue, this paper proposes an improved model based on U-Net (PL-UNet), which aims to improve the ability of UAVs to recognize power lines in complex environments in real time. To reduce the model parameters, the lightweight EfficientNetV2-S is chosen as the encoder. To address the issues of information redundancy and insufficient local structure capture caused by the skip connections, we propose a multi-scale attention gate (MSAG) in the decoding part to improve the accuracy of key region feature extraction with less computational cost. Meanwhile, the dynamic weighted fusion (DWF) module is designed to effectively fuse the features through adaptive weighting to improve the flexibility of feature expression. After completing feature fusion, we further introduce a lightweight cross-stage partial pyramid block (CPPB) module, which performs multi-scale enhancement and channel optimization of the fused features through integrating multi-scale convolutional operations and separating and fusing feature channels. Finally, the hybrid loss function of weighted cross-entropy and dice is used to solve the category imbalance problem. Comparison experiments and ablation analysis are performed on top of the power line public dataset. The proposed PL-UNet has achieved 79.98% mIoU, with a parameter count of 21.27M and a detection speed of 56.86 fps. This shows that the network has a good real-time segmentation performance. [ABSTRACT FROM AUTHOR]

Cervical cancer is a significant health issue for women and ranks fourth in the world among the most dangerous cancers. An automatic diagnostic system is needed for pap smears to assist medical experts in diagnosing cervical cancer. One of the automatic diagnosis systems in detecting cervical cancer is semantic segmentation. Convolutional Neural Networks (CNN), particularly the U-Net architecture, have been widely used for segmentation tasks in medical imaging. Although U-Net has demonstrated effectiveness, its performance on low-quality images is often suboptimal, with issues such as loss of fine details during the down-sampling process. This study combines image enhancement and Double Dropout USeg-Net (DDUSeg-Net). Image enhancement techniques are applied to pap-smear images to improve image quality such as Gamma Correction for enhanced contrast, and Median Filtering for reduced noise. The proposed DDUSeg-Net architecture builds on the U-Net model by incorporating two U-Net blocks for more detailed feature extraction. SegNet's pooling indices are added to preserve spatial information during the segmentation process. Additionally, dropout layers are introduced to prevent overfitting and reduce the model's overall complexity. The image enhancement results indicate that the Mean Squared Error (MSE), Peak Signal to Ratio (PNSR), and Structural Similarity Image Index (SSIM) are above 85%. The performance metrics for the DDUSeg-Net model obtained accuracy, precision, recall, and F1-score above 90%. This analysis used 2D pap-smear images from the Herlev dataset. Overall, the combination of image enhancement and DDUSegNet demonstrates strong robustness in the segmentation of pap-smear images, effectively balancing the detection of the intersection areas between the nucleus, cytoplasm, and background. [ABSTRACT FROM AUTHOR]

Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that utilize advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology's potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures. [ABSTRACT FROM AUTHOR]

Video saliency prediction aims to simulate human visual attention by locating the most pertinent and instructive areas within a video frame or sequence. While ignoring the audio aspect, time and space data are essential when measuring video saliency, especially with challenging factors like swift motion, changeable background, and nonrigid deformation. Additionally, video saliency detection is inappropriate when using image saliency models directly neglecting video temporal information. This paper suggests a novel Bidirectional Multi-scale SpatioTemporal Network (BMST-Net) for identifying prominent video objects to address the above problem. The BMST-Net yields notable results for any given frame sequence, employing an encoder and decoder technique to learn and map features over time and space. The BMST-Net model consists of bidirectional LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), where the VGG16 (Visual Geometry Group) single layer is used for feature extraction of the input video frames. Our proposed approach produced noteworthy findings concerning qualitative and quantitative investigation of the publicly available challenging video datasets, achieving competitive performance concerning state-of-the-art saliency models. [ABSTRACT FROM AUTHOR]

Purpose: To shorten CEST acquisition time by leveraging Z‐spectrum undersampling combined with deep learning for CEST map construction from undersampled Z‐spectra. Methods: Fisher information gain analysis identified optimal frequency offsets (termed "Fisher offsets") for the multi‐pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U‐NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient‐like cases. Results: Traditional multi‐pool models failed to quantify CEST maps from undersampled images (structural similarity index [SSIM] <0.2, peak SNR <20, Pearson r <0.1). Conversely, U‐NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z‐spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e–4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology. Conclusion: The U‐NET architecture effectively quantifies CEST maps from undersampled Z‐spectra at various undersampling levels. [ABSTRACT FROM AUTHOR]

Introduction: Orthogonal Frequency Division Multiplexing (OFDM) is widely recognized for its high efficiency in modulation techniques and has been extensively applied in underwater acoustic communication. However, the unique sparsity and noise interference characteristics of the underwater channel pose significant challenges to the performance of traditional channel estimation methods. Methods: To address these challenges, we propose a sparse underwater channel estimation method that combines the Noise2Noise (N2N) algorithm with the Sparsity Adaptive Matching Pursuit (SAMP) algorithm. This novel approach integrates the N2N technique from image denoising theory with the SAMP algorithm, utilizing a constant iteration termination threshold that does not require prior information. The method leverages the U-net neural network structure to denoise noisy pilot signals, thereby restoring channel sparsity and enhancing the accuracy of channel estimation. Results: Simulation results indicate that our proposed method demonstrates commendable channel estimation performance across various signal-to-noise ratio (SNR) conditions. Notably, in low SNR environments, the N2N-SAMP algorithm significantly outperforms the traditional SAMP algorithm in terms of Mean Squared Error (MSE) and Bit Error Rate (BER). Specifically, at SNR levels of 0 dB, 10 dB, and 20 dB, the MSE of channel estimation is reduced by 58.95%, 76.08%, and 19.42%, respectively, compared to the SAMP algorithm that selects the optimal threshold based on noise power. Furthermore, the system's BER is decreased by 12.35%, 26.41%, and 29.62%, respectively. Discussion: The findings suggest that the integration of N2N and SAMP algorithms offers a promising solution for improving channel estimation in underwater communication channels, especially under low SNR conditions. The significant reduction in MSE and BER highlights the effectiveness of our proposed method in enhancing the reliability and accuracy of underwater communication systems. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition. Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model's architecture. To address the non-stationarity of sEMG signals, we employ a windowing strategy for signal augmentation within deep learning architectures. The MobileNetV2 encoder and U-Net architecture extract relevant features from sEMG spectrogram images. Edge computing integration is leveraged to further enhance innovation by enabling real-time processing and decision-making closer to the data source. Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3–4% average accuracy improvement and a 10% variance reduction. Notably, Mendeley Data, BioPatRec DB3, and BioPatRec DB1 surpassed advanced models in their respective domains with classification accuracies of 88.71%, 90.2%, and 88.6%, respectively. Experimental results underscore the significant enhancement in generalizability and gesture recognition robustness. This approach offers a fresh perspective on prosthetic management and human-machine interaction, emphasizing its efficacy in improving accuracy and reducing variance for enhanced prosthetic control and interaction with machines through edge computing integration. [ABSTRACT FROM AUTHOR]

The most common type of malignant brain tumor, gliomas, has a variety of grades that significantly impact a patient's chance of survival. Accurate segmentation of brain tumor regions from MRI images is crucial for enhancing diagnostic precision and refining surgical strategies. This task is particularly challenging due to the diverse sizes and shapes of tumors, as well as the intricate nature of MRI data. Mastering this segmentation process is essential for improving clinical outcomes and ensuring optimal treatment planning. In this research, we provide a UNet-based model (RR-UNet) designed specifically for brain tumor segmentation, which uses small and diverse datasets containing human-annotated ground truth segmentations. This model uses residual learning to improve segmentation results over the original UNet architecture, as shown by higher dice similarity coefficient (DSC) and Intersection over Union (IoU) scores. Residual blocks enable a deeper network, which can capture complex patterns. Residual blocks reuse features, allowing the network to learn more abstract and informative representations from input images. Through comprehensive evaluation and validation, we illustrate our method's efficacy and generalization capabilities, emphasizing its potential for real-world clinical applications. This segmentation model predicts DSC of 98.18% and accuracy of 99.78% in tumor segmentation using Figshare LGG (Low-grade glioma) FLAIR segmentation dataset and DSC of 98.54% & accuracy of 99.81% using BraTS 2020 dataset. The ablation study shows the importance of the model's residual mechanism. Overall, the proposed approach outperforms or compares to existing most recent algorithms in brain tumor segmentation tasks. [ABSTRACT FROM AUTHOR]

The major challenge of edge detection in denoised images is smoothing the edges, thus confusing algorithms to classify a true edge with false one. Existing learning and optimization methods tends to handle the above problem with selectivity approach to avoid the smoothed edges. In this paper, an efficient model to detect an edges of fuzzy noise images is introduced. Inspired by U-Net and dense network, a feature map reuse and residual learning with Dense Residual Network structure (DRNet) is proposed. The feature maps from earlier blocks are dynamically combined into succeeding layers as input for the proposed DRNet. This process not only aids in spatial reconstruction, but also enhances learning efficiency by utilizing more consistent gradients to allocate the most appropriate true edge. Furthermore, the noised pixel will be eliminated with the training process for the targeted edges. To overcome the overfitting challenge in the network, this paper proposes an implementation of the DropBlock technique. The model is trained and tested using a renowned edge detection public datasets. The experimental results demonstrate that the proposed model outperformed other conventional and learning detection algorithms in terms of peak signal-to-noise ratio in different levels. These results confirm the efficacy of this study and offer a fresh approach to enhancing edge detection. [ABSTRACT FROM AUTHOR]

Background/Objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers. Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs). Subsequently, both vendor-pretrained and custom-trained models were tested on the remaining patients from each group. Auto-segmented contours were evaluated by comparing them with clinically approved contours via the Dice similarity coefficient (DSC) and mean surface distance (MSD). The performance of the left and right breast models was assessed jointly according to ipsilateral/contralateral locations. Results: The average DSCs for all structures in vendor-pretrained and custom-trained models were as follows: 0.81 ± 0.12 and 0.86 ± 0.11 in HN; 0.67 ± 0.16 and 0.80 ± 0.11 in the breast; and 0.87 ± 0.09 and 0.92 ± 0.06 in the prostate. The corresponding average MSDs were 0.81 ± 0.76 mm and 0.76 ± 0.56 mm (HN), 4.85 ± 2.44 mm and 2.42 ± 1.49 mm (breast), and 2.17 ± 1.39 mm and 1.21 ± 1.00 mm (prostate). Notably, custom-trained models showed significant improvements over vendor-pretrained models for 14 of 24 OARs, reflecting the influence of data/contouring variations in segmentation performance. Conclusions: These findings underscore the substantial impact of institutional preferences and clinical practices on the implementation of vendor-pretrained models. We also found that a relatively small amount of institutional data was sufficient to train customized segmentation models with sufficient accuracy. [ABSTRACT FROM AUTHOR]

Simple Summary: In this work, we developed three neural networks based on the U-net architecture to automatically segment the healthy liver area, the metastatic liver area, and liver metastases in micro-CT images of mice with pancreatic ductal adenocarcinoma and liver metastases. The best network for each task was then identified by cross-validation. The results demonstrated the ability of the selected networks to segment the above areas in a manner comparable to manual segmentation, at the same time saving time and ensuring reproducibility. Therefore, despite the limited number of animals involved, our pilot study represents a first step toward the development of automated tools to support liver metastasis research in the preclinical setting. Background: The liver is one of the most common sites for the spread of pancreatic ductal adenocarcinoma (PDAC) cells, with metastases present in about 80% of patients. Clinical and preclinical studies of PDAC require quantification of the liver's metastatic burden from several acquired images, which can benefit from automatic image segmentation tools. Methods: We developed three neural networks based on U-net architecture to automatically segment the healthy liver area (HL), the metastatic liver area (MLA), and liver metastases (LM) in micro-CT images of a mouse model of PDAC with liver metastasis. Three alternative U-nets were trained for each structure to be segmented following appropriate image preprocessing and the one with the highest performance was then chosen and applied for each case. Results: Good performance was achieved, with accuracy of 92.6%, 88.6%, and 91.5%, specificity of 95.5%, 93.8%, and 99.9%, Dice of 71.6%, 74.4%, and 29.9%, and negative predicted value (NPV) of 97.9%, 91.5%, and 91.5% on the pilot validation set for the chosen HL, MLA, and LM networks, respectively. Conclusions: The networks provided good performance and advantages in terms of saving time and ensuring reproducibility. [ABSTRACT FROM AUTHOR]

Medical image segmentation plays a crucial role in medical image processing, focusing on the automated extraction of regions of interest (such as organs, lesions, etc.) from medical images. This process supports various clinical applications, including diagnosis, surgical planning, and treatment. In this paper, we introduce a Boundary-guided Context Fusion U-Net (BCF-UNet), a novel approach designed to tackle a critical shortcoming in current methods: the inability to effectively integrate boundary information with semantic context. The BCF-UNet introduces a Adaptive Multi-Frequency Encoder (AMFE), which uses multi-frequency analysis inspired by the Wavelet Transform (WT) to capture both local and global features efficiently. The Adaptive Multi-Frequency Encoder (AMFE) decomposes images into different frequency components and adapts more effectively to boundary texture information through a learnable activation function. Additionally, we introduce a new multi-scale feature fusion module, the Atten-kernel Adaptive Fusion Module (AKAFM), designed to integrate deep semantic information with shallow texture details, significantly bridging the gap between features at different scales. Furthermore, each layer of the encoder sub-network integrates a Boundary-aware Pyramid Module (BAPM), which utilizes a simple and effective method and combines it with a priori knowledge to extract multi-scale edge features to improve the accuracy of boundary segmentation. In BCF-UNet, semantic context is used to guide edge information extraction, enabling the model to more effectively comprehend and identify relationships among various organizational structures. Comprehensive experimental evaluations on two datasets demonstrate that the proposed BCF-UNet achieves superior performance compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being developed, they consistently fall short when compared to the performance of human experts. This emphasizes the unique skills and expertise of human professionals in dealing with the uncertainty resulting from the vagueness and variability of MS, the lack of specificity of MRI concerning MS, and the inherent instabilities of MRI. Physicians manage this uncertainty in part by relying on their radiological, clinical, and anatomical experience. We have developed an automated framework for identifying and segmenting MS lesions in MRI scans by introducing a novel approach to replicating human diagnosis, a significant advancement in the field. This framework has the potential to revolutionize the way MS lesions are identified and segmented, being based on three main concepts: (1) Modeling the uncertainty; (2) Use of separately trained Convolutional Neural Networks (CNNs) optimized for detecting lesions, also considering their context in the brain, and to ensure spatial continuity; (3) Implementing an ensemble classifier to combine information from these CNNs. The proposed framework has been trained, validated, and tested on a single MRI modality, the FLuid-Attenuated Inversion Recovery (FLAIR) of the MSSEG benchmark public data set containing annotated data from seven expert radiologists and one ground truth. The comparison with the ground truth and each of the seven human raters demonstrates that it operates similarly to human raters. At the same time, the proposed model demonstrates more stability, effectiveness and robustness to biases than any other state-of-the-art model though using just the FLAIR modality. [ABSTRACT FROM AUTHOR]

Animation is a widespread artistic expression that holds a special place in people's hearts. Traditionally, animation creation has relied heavily on manual techniques, demanding skilled drawing abilities and a significant amount of time. For instance, many Japanese anime films draw inspiration from real-world settings, requiring access to relevant references and artists capable of translating them into anime visuals. Consequently, the development of technology that automatically converts images into anime holds great significance. Numerous methods for style transfer have been developed using unsupervised learning and have achieved impressive results. However, unsupervised learning methods suffer when the image contains multiple styles within itself because they learn the style of the image globally. To solve this problem, we propose splitting these styles within the image into multiple classes: sky, buildings, greenery, water, and other objects. Then, we style these separated classes using existing image-to-image translation models. Finally, we train a pix2pix model to learn image style transfer in a paired manner. The experimental results show that the images are effectively styled into the resulting anime-styled image domain with comparable results to existing unsupervised learning GAN-based methods. The proposed method can effectively transfer the style from real-world photos into the anime-styled image domain. [ABSTRACT FROM AUTHOR]

Skeletal muscle ultrasound has emerged as a pivotal imaging modality in rheumatology clinics, offering unparalleled advantages such as radiation-free imaging, safety, and dynamic examination capabilities. However, its reliance on operator expertise often leads to inconsistent interpretations and diagnostic variability. In this study, we present a novel diagnostic system aimed at detecting rheumatoid arthritis (RA) in the wrist joint, with a focus on identifying synovial hypertrophy and effusion using musculoskeletal ultrasound images. Leveraging deep learning techniques, specifically semantic segmentation models, we introduce SEAT-UNet, which combines the U-Net architecture with a self-attention mechanism to enhance the accuracy of lesion classification and localization. SEAT-UNet addresses the challenge of discontinuous dispersion encountered in conventional segmentation models, particularly when delineating lesion areas. Our experimental results demonstrate exceptional performance, achieving a sensitivity and Dice coefficient of 100% and 84%, respectively, in synovial hypertrophy detection, and 86% sensitivity with an 84% Dice coefficient in effusion detection. These findings underscore the potential of SEAT-UNet as a valuable tool for early RA diagnosis, offering improved patient outcomes and facilitating more effective disease management strategies. [ABSTRACT FROM AUTHOR]

Fires threaten life all over the world and damage millions of hectares of area every year. Remote sensing provides advantages for damage detection in terms of time and cost. By using satellite imagery, burned areas can be detected without the need to visit the area. Since factors such as image band configuration, optimisation algorithms, and thresholds affect the results, this study aims to observe their impact on burned area detection. Thus, by using Landsat-8 images and U-Net architecture through the Python programming language, various combinations were created and different thresholds were used. According to the results, the combination of 7, 5, 4 bands and the AdaMax algorithm were selected for the final model, and the results were improved by data augmentation. Consequently, accuracy obtained in the final model was 97.76%, which was the highest for a threshold of 0.5. The F1 score obtained for the same threshold was 79.38%. [ABSTRACT FROM AUTHOR]

Pavement scoring systems are cost-effective means of integrating the pavement management to prevent the progression of failure. The current research developed a pavement scoring system in which the presence and severity of longitudinal and transverse cracks in asphalt pavements are detected through digital images. The pavement cracks were identified using U-Net-based network architecture optimised by the grasshopper optimisation algorithm. The quantum geographic information system was used to reveal the cracks on the study zones. These zones then were evaluated, compared and scored according to their mean crack length, mean crack width and the number of cracks. The analytic hierarchy process based on expert opinion was employed to determine the importance and weight of these factors. Each area received a score based on the condition of the pavement that enabled comparison and identification of the critical areas. The proposed method can allow transportation agencies to identify and evaluate critical areas and determine the proper maintenance and repair priorities. [ABSTRACT FROM AUTHOR]