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Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. Hybrid models incorporating CNNs and transformers that can capture long-range dependencies have shown promising performance in liver segmentation with the cost of high model complexity. Therefore, a new network architecture named G-UNETR++ is proposed to improve accuracy in liver segmentation with moderate model complexity. Two gradient-based encoders that take the second-order partial derivatives (the first two elements from the last column of the Hessian matrix of a CT scan) as inputs are proposed to learn the 3D geometric features such as the boundaries between different organs and tissues. In addition, a hybrid loss function that combines dice loss, cross-entropy loss, and Hausdorff distance loss is designed to address class imbalance and improve segmentation performance in challenging cases. The proposed method was evaluated on three public datasets, the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset, and achieved 97.38%, 97.50%, and 97.32% in terms of the dice similarity coefficient for liver segmentation on the three datasets, respectively. The proposed method outperformed the other state-of-the-art models on the three datasets, which demonstrated the strong effectiveness, robustness, and generalizability of the proposed method in liver segmentation. [ABSTRACT FROM AUTHOR]

Computer-Aided Diagnostic methods demand the precise segmentation of medical images. The important stage in diagnosis is the extraction of the Region of Interest (ROI). Illustration of the image in a more meaningful way is the aim of segmentation. Segmentation finds extensive use in computer vision, object recognition, image recovery based on the content, etc. In the proposed model, Slope Variation Scatter (SVS) plot of image is obtained to compute vertices of Neutrosophic Gaussian Function (NGF). The SVS describes global variation rate of image histogram. In this, the crests represent local mean of pixels/ certainty mean and valleys represents uncertainty mean. A novel NGF is membership function designed to convert abdominal Computed Tomography (CT) to Neutrosophic Subsets (NS). The NS comprises of object, nonobject and edge subsets. The Object Subset (OS) represents liver or kidney, Nonobject Subset (NOS) represents background of liver/kidney and Edge Subset (ES) represents edges of liver or kidney. The proposed model is experimented on 106 abdominal CT images to segment the liver and kidney accurately. The experimental outcomes are compared with Fuzzy C Means algorithm (FCM), show that the anticipated framework is proficient of segmenting an intended organ automatically and precisely. The proposed model achieves average accuracy, Relative Volume Difference (RVD) and Dice Similarity Factor (DSF) for liver are 91.01%, 8.23% and 89.61% respectively. The average accuracy, RVD and DSF for kidney are 91.11%, 5.96% and 91.45% respectively. [ABSTRACT FROM AUTHOR]

Medical image analysis requires liver segmentation for liver disease detection and treatment. Deep learning approaches, particularly liver segmentation, have demonstrated astounding effectiveness in a variety of medical imaging applications. Using the U-Net architecture, a well-liked and successful deep learning model for semantic segmentation, a liver segmentation approach is suggested in this study. This approach uses 3D abdominal CT images with liver regions identified. The U-Net model collects local and global contextual data via skip links and an encoder-decoder network. Supervised learning and data augmentation are used to develop the network's generalization ability. Intensity normalization, voxel resampling, and image cropping were used to enhance liver segmentation by improving input data quality and consistency. Post-processing approaches like linked component analysis and morphology improved segmentation results and eliminated false positives. A separate test dataset and conventional assessment criteria as DSC, sensitivity, and specificity were employed to evaluate our liver segmentation approach. A Dice score of 0.9287 indicates a 92.87% overlap between the sets. This is a good result since the segmentation or comparison approach identified and aligned the matching regions in the sets. Train dice loss, train metric dice, test dice loss, test metric dice and mean dice are found to be 0.0223, 0.9733, 0.289, 0.782, and 0.9287 respectively. Lab results reveal that the current liver segmentation approach is accurate and resilient. Comparing present strategy to other cutting-edge liver segmentation methods shows its competitiveness. In conclusion, this study proposes a liver segmentation method based on the U-Net architecture that successfully tackles the difficulties in precisely distinguishing the liver from abdominal CT scans. The suggested method has produced encouraging results, demonstrating its potential for clinical uses in the diagnosis of liver disease, surgical planning, and therapy monitoring. [ABSTRACT FROM AUTHOR]

Background: In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Currently this is performed manually, generally by trained radiologists, who are seeing an inexorable growth in their workload. Integrating artificial intelligence (AI) and non-radiologist personnel into the workflow potentially addresses the increasing workload without sacrificing accuracy. This study evaluated the accuracy of non-radiologist technicians in liver cancer imaging compared with radiologists, both assisted by AI. Methods: Non-contrast T1-weighted MRI data from 18 colorectal liver metastasis patients were analyzed using an AI-enabled decision support tool that enables non-radiology trained technicians to perform key liver measurements. Three non-radiologist, experienced operators and three radiologists performed whole liver segmentation, Couinaud segment segmentation, and the detection and measurements of lesions aided by AI-generated delineations. Agreement between radiologists and non-radiologists was assessed using the intraclass correlation coefficient (ICC). Two additional radiologists adjudicated any lesion detection discrepancies. Results: Whole liver volume showed high levels of agreement between the non-radiologist and radiologist groups (ICC = 0.99). The Couinaud segment volumetry ICC range was 0.77–0.96. Both groups identified the same 41 lesions. As well, the non-radiologist group identified seven more structures which were also confirmed as lesions by the adjudicators. Lesion diameter categorization agreement was 90%, Couinaud localization 91.9%. Within-group variability was comparable for lesion measurements. Conclusion: With AI assistance, non-radiologist experienced operators showed good agreement with radiologists for quantifying whole liver volume, Couinaud segment volume, and the detection and measurement of lesions in patients with known liver cancer. This AI-assisted non-radiologist approach has potential to reduce the stress on radiologists without compromising accuracy. [ABSTRACT FROM AUTHOR]

Background/Objectives: Accurate liver and tumor detection and segmentation are crucial in diagnosis of early-stage liver malignancies. As opposed to manual interpretation, which is a difficult and time-consuming process, accurate tumor detection using a computer-aided diagnosis system can save both time and human efforts. Methods: We propose a cascaded encoder–decoder technique based on self-organized neural networks, which is a recent variant of operational neural networks (ONNs), for accurate segmentation and identification of liver tumors. The first encoder–decoder CNN segments the liver. For generating the liver region of interest, the segmented liver mask is placed over the input computed tomography (CT) image and then fed to the second Self-ONN model for tumor segmentation. For further investigation the other three distinct encoder–decoder architectures U-Net, feature pyramid networks (FPNs), and U-Net++, have also been investigated by altering the backbone at the encoders utilizing ResNet and DenseNet variants for transfer learning. Results: For the liver segmentation task, Self-ONN with a ResNet18 backbone has achieved a dice similarity coefficient score of 98.182% and an intersection over union of 97.436%. Tumor segmentation with Self-ONN with the DenseNet201 encoder resulted in an outstanding DSC of 92.836% and IoU of 91.748%. Conclusions: The suggested method is capable of precisely locating liver tumors of various sizes and shapes, including tiny infection patches that were said to be challenging to find in earlier research. [ABSTRACT FROM AUTHOR]

The lack of labeled, intraoperative patient data in medical scenarios poses a relevant challenge for machine learning applications. Given the apparent power of machine learning, this study examines how synthetically-generated data can help to reduce the amount of clinical data needed for robust liver surface segmentation in laparoscopic images. Here, we report the results of three experiments, using 525 annotated clinical images from 5 patients alongside 20,000 synthetic photo-realistic images from 10 patient models. The effectiveness of the use of synthetic data is compared to the use of data augmentation, a traditional performance-enhancing technique. For training, a supervised approach employing the U-Net architecture was chosen. The results of these experiments show a progressive increase in accuracy. Our base experiment on clinical data yielded an F1 score of 0.72. Applying data augmentation to this model increased the F1 score to 0.76. Our model pre-trained on synthetic data and fine-tuned with augmented data achieved an F1 score of 0.80, a 4% increase. Additionally, a model evaluation involving k-fold cross validation highlighted the dependency of the result on the test set. These results demonstrate that leveraging synthetic data has the ability of limiting the need for more patient data to increase the segmentation performance. [ABSTRACT FROM AUTHOR]

Abstract Due to the low contrast of abdominal CT (Computer Tomography) images and the similar color and shape of the liver to other organs such as the spleen, stomach, and kidneys, liver segmentation presents significant challenges. Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) to improve liver feature learning and thereby enhance liver segmentation performance. Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. The MSFF module enhances the capture of global features, thus improving the accuracy of the liver segmentation model. Through the aforementioned research, this paper proposes a liver segmentation network based on detail enhancement and multi-scale feature fusion (DEMF-Net). We conducted extensive experiments on the LiTS17 dataset, and the results demonstrate that the DEMF-Net model achieved significant improvements across various evaluation metrics. Therefore, the proposed DEMF-Net model can achieve precise liver segmentation.

Abstract Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture’s testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance.

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture's testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance. [ABSTRACT FROM AUTHOR]

Simple Summary: The measurement of canine liver size is essential, particularly in the evaluation of hepatic disease. Computed tomography (CT)-based liver volumetry can be useful for the assessment of liver size, but the reference interval has not been reported in dogs without hepatic disease. The purpose of the present study was to define the reference interval for CT-based liver volume normalized by body weight in dogs with no history of hepatic disease. The weight-based reference interval lower limit of 11.1–15.5 (90% confidence interval [CI]) to an upper limit of 31.9–42.6 (90% CI) cm3/kg was defined by evaluating CT scans of 121 dogs with no history of hepatic disease. This weight-based reference interval provides an accurate assessment of liver volume changes in dogs with various hepatic diseases, thereby facilitating the diagnosis and management of hepatic disease in veterinary medicine. In both human and veterinary medicine, computed tomography (CT) volumetry provides a quantitative and accurate measure of liver volume. While CT volumetry is recognized as a useful method for assessing liver volume in dogs, a statistically significant reference interval for liver volume in dogs with no history of hepatic disease has not been reported. The purpose of the present study was to define a reference interval for liver volume with no history of hepatic disease using CT volumetry. Medical records from 2 June 2020 to 25 July 2022 were retrospectively reviewed, including 121 dogs that underwent abdominal CT scans and had no history of hepatic disease. Liver volumes were measured using CT volumetry and normalized by body weight. The median of normalized CT-based liver volume in 121 dogs was 22.2 cm3/kg. Based on these data, a weight-based reference interval lower limit of 11.1–15.5 (90% confidence interval [CI]) to an upper limit of 31.9–42.6 (90% CI) cm3/kg for CT-based liver volume was defined in dogs without hepatic disease. This study provides an accurate assessment of liver volume changes in dogs with various hepatic diseases. [ABSTRACT FROM AUTHOR]

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks. [ABSTRACT FROM AUTHOR]

The liver is a vital organ in the human body, and CT images can intuitively display its morphology. Physicians rely on liver CT images to observe its anatomical structure and areas of pathology, providing evidence for clinical diagnosis and treatment planning. To assist physicians in making accurate judgments, artificial intelligence techniques are adopted. Addressing the limitations of existing methods in liver CT image segmentation, such as weak contextual analysis and semantic information loss, we propose a novel Dual Attention-Based 3D U-Net liver segmentation algorithm on CT images. The innovations of our approach are summarized as follows: (1) We improve the 3D U-Net network by introducing residual connections to better capture multi-scale information and alleviate semantic information loss. (2) We propose the DA-Block encoder structure to enhance feature extraction capability. (3) We introduce the CBAM module into skip connections to optimize feature transmission in the encoder, reducing semantic gaps and achieving accurate liver segmentation. To validate the effectiveness of the algorithm, experiments were conducted on the LiTS dataset. The results showed that the Dice coefficient and HD95 index for liver images were 92.56% and 28.09 mm, respectively, representing an improvement of 0.84% and a reduction of 2.45 mm compared to 3D Res-UNet. [ABSTRACT FROM AUTHOR]

Liver segmentation is a crucial step in medical image analysis and is essential for diagnosing and treating liver diseases. However, manual segmentation is time‐consuming and subject to variability among observers. To address these challenges, a novel liver segmentation approach, SwinUNet with transformer skip‐fusion is proposed. This method harnesses the Swin Transformer's capacity to model long‐range dependencies efficiently, the U‐Net's ability to preserve fine spatial details, and the transformer skip‐fusion's effectiveness in enabling the decoder to learn intricate features from encoder feature maps. In experiments using the 3DIRCADb and CHAOS datasets, this technique outperformed traditional CNN‐based methods, achieving a mean DICE coefficient of 0.988% and a mean Jaccard coefficient of 0.973% by aggregating the results obtained from each dataset, signifying outstanding agreement with ground truth. This remarkable accuracy in liver segmentation holds significant promise for improving liver disease diagnosis and enhancing healthcare outcomes for patients with liver conditions. [ABSTRACT FROM AUTHOR]

Accurate liver segmentation from computed tomography (CT) scans is essential for liver cancer diagnosis and liver surgery planning. Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. Hybrid models incorporating CNNs and transformers that can capture long-range dependencies have shown promising performance in liver segmentation with the cost of high model complexity. Therefore, a new network architecture named G-UNETR++ is proposed to improve accuracy in liver segmentation with moderate model complexity. Two gradient-based encoders that take the second-order partial derivatives (the first two elements from the last column of the Hessian matrix of a CT scan) as inputs are proposed to learn the 3D geometric features such as the boundaries between different organs and tissues. In addition, a hybrid loss function that combines dice loss, cross-entropy loss, and Hausdorff distance loss is designed to address class imbalance and improve segmentation performance in challenging cases. The proposed method was evaluated on three public datasets, the Liver Tumor Segmentation (LiTS) dataset, the 3D Image Reconstruction for Comparison of Algorithms Database (3D-IRCADb), and the Segmentation of the Liver Competition 2007 (Sliver07) dataset, and achieved 97.38%, 97.50%, and 97.32% in terms of the dice similarity coefficient for liver segmentation on the three datasets, respectively. The proposed method outperformed the other state-of-the-art models on the three datasets, which demonstrated the strong effectiveness, robustness, and generalizability of the proposed method in liver segmentation.

Background: Semi‐supervised learning has gained popularity in medical image segmentation due to its ability to reduce reliance on image annotation. A typical approach in semi‐supervised learning is to select reliable predictions as pseudo‐labels and eliminate unreliable predictions. Contrastive learning helps prevent the insufficient utilization of unreliable predictions, but neglecting the anatomical structure of medical images can lead to suboptimal optimization results. Purpose: We propose a novel approach for semi‐supervised liver segmentation using contrastive learning, which leverages unlabeled data and enhances the suitability of contrastive learning for liver segmentation. Method and materials: Contrastive learning helps prevent the inappropriate utilization of unreliable predictions, but neglecting the anatomical structure of medical images can lead to suboptimal optimization results. Therefore, we propose a semi‐supervised contrastive learning method with local regions self‐supervision (LRS2). On one side, we employ Shannon entropy to distinguish between reliable and unreliable predictions and reduce the dissimilarity between their representations within regional artificial units. Within each unit of the liver image, we strongly encourage unreliable predictions to acquire valuable information pertaining to the correct category by leveraging the representations of reliable predictions in their vicinity. On the other side, we introduce a dynamic reliability threshold based on the Shannon entropy of each prediction, gradually evaluating the confidence threshold of reliable predictions as predictive accuracy improves. After selecting reliable predictions, we sequentially apply erosion and dilation to refine them for better selection of qualified positive and negative samples. We evaluate our proposed method on abdominal CT images, including 131 images (train data: 77, validation data: 26, and testing data: 28) from 2017 ISBI Liver Tumors Segmentation Challenge. Results: Our method obtains satisfactory performance in different proportion by exploiting the unreliable predictions. Compared with the result of VNet only under supervised settings (with 10, 30, 50, 70% and full labeled data), LRS2, respectively, brings an improvement of Dice coefficient by +6.11, +3.55, +4.43, and +2.25%, achieving Dice coefficients of 93.44, 93.31, 94.85, and 95.12%, respectively. Conclusion: In this study, we carefully select appropriate positive and negative samples from reliable regions, ensuring that anchor pixels within unreliable regions are correctly assigned to their respective categories. With a consideration of the anatomical structure present in CT images, we partition the image representations into regional units, enabling anchor pixels to capture more precise sample information. Extensive experiments confirm the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. [ABSTRACT FROM AUTHOR]

An abdominal Computed Tomography (CT) scan gives more information about diseases of the liver, gallbladder, and biliary tract. In the image processing approach, liver segmentation is an essential step to be done before liver lesion detection. Liver segmentation removes the unwanted regions from the CT image and makes the task of lesion detection easier. In this paper, a novel Morph-Rec model based on morphological reconstruction operation is proposed for liver segmentation from CT images. The proposed work is focused on segmenting the liver region from the CT slices irrespective of the size and shape of the liver region. The proposed model is validated on 2650 CT slices of 120 and 20 CT scans from the LITS and 3DIRCADb datasets, respectively. The proposed Morph-Rec method is evaluated using metrics such as dice score, accuracy, F1 score, Jaccard index and Matthew's correlation coefficient. To justify the adaptability and efficiency of the proposed model, it is also validated on 50 CT slices of nine CT scans provided by a local scan centre. The proposed method has produced excellent results on all metrics and the obtained results are better than the state-of-the-art conventional methods for liver segmentation. Hence, the proposed technique is an automatic and dataset-generic model that can perform liver segmentation precisely on any CT acquisition of the liver. [ABSTRACT FROM AUTHOR]

In both human and veterinary medicine, computed tomography (CT) volumetry provides a quantitative and accurate measure of liver volume. While CT volumetry is recognized as a useful method for assessing liver volume in dogs, a statistically significant reference interval for liver volume in dogs with no history of hepatic disease has not been reported. The purpose of the present study was to define a reference interval for liver volume with no history of hepatic disease using CT volumetry. Medical records from 2 June 2020 to 25 July 2022 were retrospectively reviewed, including 121 dogs that underwent abdominal CT scans and had no history of hepatic disease. Liver volumes were measured using CT volumetry and normalized by body weight. The median of normalized CT-based liver volume in 121 dogs was 22.2 cm3/kg. Based on these data, a weight-based reference interval lower limit of 11.1–15.5 (90% confidence interval [CI]) to an upper limit of 31.9–42.6 (90% CI) cm3/kg for CT-based liver volume was defined in dogs without hepatic disease. This study provides an accurate assessment of liver volume changes in dogs with various hepatic diseases.

Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI'2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes. [ABSTRACT FROM AUTHOR]

Introduction: The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels. Methods: To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training. Results and discussion: Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use. [ABSTRACT FROM AUTHOR]

Cancer is a devastating and deadly disease, and liver cancer is one of the leading causes of cancer deaths. Early detection of liver tumor is important to choose a treatment plan, get an accurate prognosis, and gain a deep understanding of the tumor to determine its severity. Despite a lot of research, performing automatic segmentation and classification liver tumor is still a challenging task due to the low tissue contrast between the surrounding organs and the deformable shape of the CT image. Therefore, this paper introduces the unified learning multi-task model network for combined automatic liver tumor segmentation and classification. The first step is to build a multi-task deformable attention U-Net (MDAUnet) technique to segment the liver tumor and capture the features for classification. Here, an attention-based deformable module is used instead of convolution to learn the irregular and inconspicuous appearance of tumors by combining context attention and deformable convolution. Further, a residual skip connection is used to avoid duplicate transmission of low-resolution data by introducing a residual path. In the second step, the segmented liver tumor features from MDAUnet are fed into the deep DenseNet (DDNet) model and concatenation layer. Based on the segmented liver tumor features, DDNet learns distinguishable features for classification. The concatenation layer combines the learned features of the MDAUnet and DDNet models for liver tumor classification. Finally, a fully connected layer classifies primary and secondary liver tumors. Therefore, our proposed ULM-net model outperforms single models in terms of precision, F-1 measure, recall, classification accuracy, and kappa coefficient. [ABSTRACT FROM AUTHOR]

Aim: To evaluate various segmentation software for liver segmentation from MR images. Method: Three MR examinations were performed on seven healthy volunteers (average age 38.2 ± 5.5 years, BMI = 28.6 ± 8.3 kg/m²) without known liver or cholestatic diseases -- before initiating fasting, after 48 hours of fasting, and after subsequent twoday carbohydrate realimentation. The livers were segmented using seven (semi)automatic methods of software 3D Slicer, LiverLab, ITKSNAP, Myrian and MedSeg and compared to the reference manual segmentation. Results: All methods used for liver volume determination showed good accuracy. Intraclass coefficients of consistency and agreement were above 0.95. The TotalSegmentator module in the 3D Slicer program achieved the best coefficient of variation (CV), and also demonstrated the highest accuracy in the individual assessment of the dietary intervention effect, with an average CV below 10% (other methods ranged from 10-20%). Conclusion: 3D Slicer can be considered the best among all the tested segmentation software for liver segmentation from MR images in terms of program availability, accuracy, and speed. In basic tasks such as organ segmentation, it can compete with commercial software. It can accurately track liver volume changes during short-term dietary interventions. [ABSTRACT FROM AUTHOR]

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.

Accurate segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC) patients. However, manual segmentation is time-consuming and subject to inter- and intra-rater variability. Therefore, automated methods are necessary but require rigorous validation of high-quality segmentations based on a consensus of raters. To address the need for reliable and comprehensive data in this domain, we present LiverHccSeg, a dataset that provides liver and tumor segmentations on multiphasic contrast-enhanced magnetic resonance imaging from two board-approved abdominal radiologists, along with an analysis of inter-rater agreement.LiverHccSeg provides a curated resource for liver and HCC tumor segmentation tasks. The dataset includes a scientific reading and co-registered contrast-enhanced multiphasic magnetic resonance imaging (MRI) scans with corresponding manual segmentations by two board-approved abdominal radiologists and relevant metadata and offers researchers a comprehensive foundation for external validation, and benchmarking of liver and tumor segmentation algorithms. The dataset also provides an analysis of the agreement between the two sets of liver and tumor segmentations. Through the calculation of appropriate segmentation metrics, we provide insights into the consistency and variability in liver and tumor segmentations among the radiologists. A total of 17 cases were included for liver segmentation and 14 cases for HCC tumor segmentation. Liver segmentations demonstrates high segmentation agreement (mean Dice, 0.95 ± 0.01 [standard deviation]) and HCC tumor segmentations showed higher variation (mean Dice, 0.85 ± 0.16 [standard deviation]).The applications of LiverHccSeg can be manifold, ranging from testing machine learning algorithms on public external data to radiomic feature analyses. Leveraging the inter-rater agreement analysis within the dataset, researchers can investigate the impact of variability on segmentation performance and explore methods to enhance the accuracy and robustness of liver and tumor segmentation algorithms in HCC patients. By making this dataset publicly available, LiverHccSeg aims to foster collaborations, facilitate innovative solutions, and ultimately improve patient outcomes in the diagnosis and treatment of HCC.

Automatic segmentation of the liver in abdominal CT images is critical for guiding liver cancer biopsies and treatment planning. Yet, automatic segmentation of CT liver images remains challenging due to the poor contrast between the liver and surrounding organs in abdominal CT images. In this paper, we propose a novel network for liver segmentation, and the network is essentially a U-shaped network with an encoder–decoder structure. Firstly, the complementary feature enhancement unit is designed in the network to mitigate the semantic gap between encoder and decoder. The complementary feature enhancement unit is based on subtraction, which enhances the complementary features between encoder and decoder. Secondly, this paper proposes a new cross attention model that no longer generates value by convolution, which reduces redundant information and enhances the contextual information of single sparse attention by encoding contextual information by 3 × 3 convolution. The dice score, accuracy, and precision of our network on the LiTS dataset were 95.85 % , 97.19 % , and 97.11 % , and the dice score, accuracy, and precision on the dataset consisted of 3Dircadb and CHAOS were 93.65 % , 94.38 % , and 97.53 % . [ABSTRACT FROM AUTHOR]

Liver segmentation is a prolific and important area of research that has been deeply studied for the last three decades. Its prominence is increasing in modern Computer-Aided disease Diagnosis (CAD) to deal with a huge amount of images. In this paper, the CT scan image is resized to 256 X 256, and noise is reduced by a median filter, and then local peaks are acquired. The optimal clusters (k) to be formed by Expectation-Maximization (EM) algorithm are obtained by setting the distance between local peaks and height greater than 5. Formulate k number of clusters using the EM algorithm. Crop random section of liver and obtain all the local peaks greater than average of local peaks. This provides the minimum and maximum threshold values using which a threshold-based segmentation is performed. The anticipated algorithm that is verified on 55 CT scan images offers promising results. The experimental outcomes are compared with the existing cluster-based liver segmentation algorithms. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses spatial and channel features during segmentation, leading to inaccurate liver and LT segmentation. A residual deformable split depth-wise separable U-Net (RDSDSU-Net) is proposed to increase the accuracy of liver and LT segmentation. The residual deformable convolution layer (DCL) with deformable pooling (DP) is used in the encoder as an attention mechanism to adaptively extract liver and LT shape and position characteristics. Afterward, a convolutional spatial and channel features split graph network (CSCFSG-Net) is introduced in the middle processing layer to improve the expression capability of the liver and LT features by capturing spatial and channel features separately and to extract global contextual liver and LT information from spatial and channel features. Sub-pixel convolutions (SPC) are used in the decoder section to prevent the segmentation results from having a chequerboard artefact effect. Also, the residual deformable encoder features are combined with the decoder through summation to avoid increasing the number of feature maps (FM). Finally, the efficiency of the RDSDSU-Net is evaluated on the 3DIRCADb and LiTS datasets. The DICE score of the proposed RDSDSU-Net achieved 98.21% for liver segmentation and 93.25% for LT segmentation on 3DIRCADb. The experimental outcomes illustrate that the proposed RDSDSU-Net model achieved better segmentation results than the existing techniques. [ABSTRACT FROM AUTHOR]

Segmenting the liver and liver tumors in computed tomography (CT) images is an important step toward quantifiable biomarkers for a computer-aided decision-making system and precise medical diagnosis. Radiologists and specialized physicians use CT images to diagnose and classify liver organs and tumors. Because these organs have similar characteristics in form, texture, and light intensity values, other internal organs such as the heart, spleen, stomach, and kidneys confuse visual recognition of the liver and tumor division. Furthermore, visual identification of liver tumors is time-consuming, complicated, and error-prone, and incorrect diagnosis and segmentation can hurt the patient's life. Many automatic and semi-automatic methods based on machine learning algorithms have recently been suggested for liver organ recognition and tumor segmentation. However, there are still difficulties due to poor recognition precision and speed and a lack of dependability. This paper presents a novel deep learning-based technique for segmenting liver tumors and identifying liver organs in computed tomography maps. Based on the LiTS17 database, the suggested technique comprises four Chebyshev graph convolution layers and a fully connected layer that can accurately segment the liver and liver tumors. Thus, the accuracy, Dice coefficient, mean IoU, sensitivity, precision, and recall obtained based on the proposed method according to the LiTS17 dataset are around 99.1%, 91.1%, 90.8%, 99.4%, 99.4%, and 91.2%, respectively. In addition, the effectiveness of the proposed method was evaluated in a noisy environment, and the proposed network could withstand a wide range of environmental signal-to-noise ratios (SNRs). Thus, at SNR = −4 dB, the accuracy of the proposed method for liver organ segmentation remained around 90%. The proposed model has obtained satisfactory and favorable results compared to previous research. According to the positive results, the proposed model is expected to be used to assist radiologists and specialist doctors in the near future. [ABSTRACT FROM AUTHOR]

Abstract Deep learning image segmentation has become an important field of interest in recent years, especially when it comes to medical images. Segmentation of medical image modalities such as magnetic resonance imaging (MRI) and computed tomography (CT) can benefit diagnosis accuracy, speed up diagnosis process, and decrease workload. The most famously used deep learning models in the medical image segmentation are the UNET‐based models, which have been repeatedly proven to provide a high percentage of accuracy in medical image segmentation. But, most of the available datasets contain a single modality and thus are not big enough to train complex architectures. Lately, it has been shown that using multiple modalities with multiple streams architectures can provide higher accuracy more than single modality with a single stream architecture. In this paper, the benefits of dual‐stream and triple‐stream architectures are demonstrated when processing multiple modalities. This work shows that dual stream can achieve dice of 0.97 on CT images and 0.89 on MRI images, while in triple stream architectures can achieve dice of 0.97 on CT images and 0.96 on MRI images. To the best of our knowledge, these are the best results to date.

Medical image segmentation of the liver is an important prerequisite for clinical diagnosis and evaluation of liver cancer. For automatic liver segmentation from Computed Tomography (CT) images, we proposed a Multi-scale Feature Extraction and Enhancement U-Net (mfeeU-Net), incorporating Res2Net blocks, Squeeze-and-Excitation (SE) blocks, and Edge Attention (EA) blocks. The Res2Net blocks which are conducive to extracting multi-scale features of the liver were used as the backbone of the encoder, while the SE blocks were also added to the encoder to enhance channel information. The EA blocks were introduced to skip connections between the encoder and the decoder, to facilitate the detection of blurred liver edges where the intensities of nearby organs are close to the liver. The proposed mfeeU-Net was trained and evaluated using a publicly available CT dataset of LiTS2017. The average dice similarity coefficient, intersection-over-union ratio, and sensitivity of the mfeeU-Net for liver segmentation were 95.32%, 91.67%, and 95.53%, respectively, and all these metrics were better than those of U-Net, Res-U-Net, and Attention U-Net. The experimental results demonstrate that the mfeeU-Net can compete with and even outperform recently proposed convolutional neural networks and effectively overcome challenges, such as discontinuous liver regions and fuzzy liver boundaries.

Background: Automatic liver segmentation is a prerequisite for hepatoma treatment; however, the low accuracy and stability hinder its clinical application. To alleviate this limitation, we deeply mine the context information of different scales and combine it with deep supervision to improve the accuracy of liver segmentation in this paper. Methods: We proposed a new network called MAD-UNet for automatic liver segmentation from CT. It is grounded in the 3D UNet and leverages multi-scale attention and deep supervision mechanisms. In the encoder, the downsampling pooling in 3D UNet is replaced by convolution to alleviate the loss of feature information. Meanwhile, the residual module is introduced to avoid gradient vanishment. Besides, we use the long-short skip connections (LSSC) to replace the ordinary skip connections to preserve more edge detail. In the decoder, the features of different scales are aggregated, and the attention module is employed to capture the spatial context information. Moreover, we utilized the deep supervision mechanism to improve the learning ability on deep and shallow information. Results: We evaluated the proposed method on three public datasets, including, LiTS17, SLiver07, and 3DIRCADb, and obtained Dice scores of 0.9727, 0.9752, and 0.9691 for liver segmentation, respectively, which outperform the other state-of-the-art (SOTA) methods. Conclusions: Both qualitative and quantitative experimental results demonstrate that the proposed method can make full use of the feature information of different stages while enhancing spatial data's learning ability, thereby achieving high liver segmentation accuracy. Thus, it proved to be a promising tool for automatic liver segmentation in clinical assistance.

Automatic liver cancer detection (ALCD) is very crucial in automatic biomedical image analysis and diagnosis as it is the largest organ in the body and plays a significant role in the metabolic process as well as the elimination of toxins. In the last decade, various machine and deep learning schemes have been investigated for automatic ALCD using computed tomography (CT) images. However, ALCD in CT images is challenging because of the noise, intricate structure of abdominal computed tomography (CT) images, and textural changes throughout the CT images making liver segmentation a vital challenge that may result in both under-segmentation (u-seg) and over-segmentation ( o-seg) of the organ. This paper presents liver segmentation based on the proposed Edge Strengthening Parallel UNet (ESP-UNet) for liver segmentation to avoid the u-seg and o-seg of the liver in CT images. Further, it offered ALCD based on lightweight sequential Deep Convolution Neural Networks (DCNN). The consequences of ESP-UNet DCNN-based ALCD are evaluated based on accuracy, recall, precision, and F1-score. The suggested approach provides a noteworthy improvement in ALCD over the traditional state of arts.

Multi-phase computed tomography (CT) images have gained significant popularity in the diagnosis of hepatic disease. There are several challenges in the liver segmentation of multi-phase CT images. (1) Annotation: due to the distinct contrast enhancements observed in different phases (i.e., each phase is considered a different domain), annotating all phase images in multi-phase CT images for liver or tumor segmentation is a task that consumes substantial time and labor resources. (2) Poor contrast: some phase images may have poor contrast, making it difficult to distinguish the liver boundary. In this paper, we propose a boundary-enhanced liver segmentation network for multi-phase CT images with unsupervised domain adaptation. The first contribution is that we propose DD-UDA, a dual discriminator-based unsupervised domain adaptation, for liver segmentation on multi-phase images without multi-phase annotations, effectively tackling the annotation problem. To improve accuracy by reducing distribution differences between the source and target domains, we perform domain adaptation at two levels by employing two discriminators, one at the feature level and the other at the output level. The second contribution is that we introduce an additional boundary-enhanced decoder to the encoder–decoder backbone segmentation network to effectively recognize the boundary region, thereby addressing the problem of poor contrast. In our study, we employ the public LiTS dataset as the source domain and our private MPCT-FLLs dataset as the target domain. The experimental findings validate the efficacy of our proposed methods, producing substantially improved results when tested on each phase of the multi-phase CT image even without the multi-phase annotations. As evaluated on the MPCT-FLLs dataset, the existing baseline (UDA) method achieved IoU scores of 0.785, 0.796, and 0.772 for the PV, ART, and NC phases, respectively, while our proposed approach exhibited superior performance, surpassing both the baseline and other state-of-the-art methods. Notably, our method achieved remarkable IoU scores of 0.823, 0.811, and 0.800 for the PV, ART, and NC phases, respectively, emphasizing its effectiveness in achieving accurate image segmentation. [ABSTRACT FROM AUTHOR]

Purpose: Automatic liver segmentation is a key component for performing computer-assisted hepatic procedures. The task is challenging due to the high variability in organ appearance, numerous imaging modalities, and limited availability of labels. Moreover, strong generalization performance is required in real-world scenarios. However, existing supervised methods cannot be applied to data not seen during training (i.e. in the wild) because they generalize poorly. Methods: We propose to distill knowledge from a powerful model with our novel contrastive distillation scheme. We use a pre-trained large neural network to train our smaller model. A key novelty is to map neighboring slices close together in the latent representation, while mapping distant slices far away. Then, we use ground-truth labels to learn a U-Net style upsampling path and recover the segmentation map. Results: The pipeline is proven to be robust enough to perform state-of-the-art inference on target unseen domains. We carried out an extensive experimental validation using six common abdominal datasets, covering multiple modalities, as well as 18 patient datasets from the Innsbruck University Hospital. A sub-second inference time and a data-efficient training pipeline make it possible to scale our method to real-world conditions. Conclusion: We propose a novel contrastive distillation scheme for automatic liver segmentation. A limited set of assumptions and superior performance to state-of-the-art techniques make our method a candidate for application to real-world scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Liver segmentation is an essential step in a variety of clinical applications like tumor detection, transplantation, and other liver treatments. Even though there is a lot of work carried out, it is still a challenging task to segment a single elliptical liver structure from a roughly distributed similar structure or shape in the abdomen using computerized tomography (CT) images. The existing methods are unable to accurately extract the liver image boundary due to the extraction of low-level features, inaccuracy, and over-fitting. Method: A 3D Sparse Deep Belief Network with Enriched Seagull Optimization (3D-SDBN-ESO) is proposed that extracts the accurate elliptical liver structure. The abdominal CT images are first fed into a preprocessing stage that uses Gaussian filtering and contrast local adaptive histogram equalization in the liver segmentation process. After preprocessing, the proposed 3D-SDBN segments the liver by extracting high-level spatiotemporal features. This model effectively learns high-level contextual knowledge and enhances the performance and network capacity of the model while performing feature representation. However, it is difficult to determine the 3D-SDBN parameters that significantly affect segmentation accuracy and training loss. To solve this problem, the number of hidden nodes, learning rate, and mini batch size of the 3D-SDBN are optimized utilizing Enriched Seagull Optimization (ESO). Moreover, the exact boundary of the liver is determined from the segmented images using Localized Active Contour (LAC) by reducing fuzzy boundary interference. Results: Experimental analysis is conducted with different metrics such as the Dice Coefficient (DI), Jacaard Index (JI), Volumetric Overlap Error (VOE), Overlap Coefficient (OC), Relative Volume Error (RVE), Average Symmetric Surface Distance (ASD), and Dice Similarity Coefficient (DSC) on two publicly available datasets such as the 3Dircadb01 and LITS datasets. The performance of the proposed 3D-SDBN-ESO approach obtains high estimation values of 97.19, 99.73, 99.69, and 96.1 in terms of DI, JI, OC, and DSC on the 3Dircadb01 dataset, respectively. Also, it achieves high estimation results of 96.31, 92.89, 98.02, and 96.6 in terms of DI, JI, OC, and DSC on LITS datasets, respectively. Conclusion: The overall results demonstrate that the proposed 3D-SDBN-ESO accurately segments the liver from abdominal CT images compared to existing methods and solves the challenging problems. [ABSTRACT FROM AUTHOR]

Automatic segmentation of the liver from CT images is a very challenging task because the shape of the liver in the abdominal cavity varies from person to person and it also often fits closely with other organs. In recent years, with the continuous development of deep learning and the proposal of CNN, the neural network-based segmentation models have shown good performance in the field of image segmentation. Among the many network models, U-Net stands out in the task of medical image segmentation. In this paper, we propose a segmentation network MSAA-Net combining multi-scale features and an improved attention-aware U-Net. We extracted features of different scales on a single feature layer and performed attention perception in the channel dimension. We demonstrate that this architecture improves the performance of U-Net, while significantly reducing computational costs. To address the problem that U-Net's skip connection is difficult to optimize for merging objects of different sizes, we designed a multi-scale attention gate structure (MAG), which allows the model to automatically learn to focus on targets of different sizes. In addition, MAG can be extended to all structures which contain skip connections, such as U-Net and FCN variants. Our structure was extensively evaluated on the 3Dircadb dataset, and the DICE similarity coefficient of the method for the liver segmentation task was 94.42%, with a much smaller number of model parameters than other attentional models. The experimental results show that MSAA-Net achieves very competitive performance in liver segmentation. [ABSTRACT FROM AUTHOR]