CoProLITE: Constrained Proxy Learning for lIver and hepaTic lesion sEgmentation

Introduction: Precise segmentation of the liver from computed tomography (CT) scans is an essential component in the diagnosis and treatment of liver diseases such as hepatocellular carcinoma. Manual segmentation can be time-consuming and subject to variability, spurring the application of automated systems based on deep learning. We introduce a 3D U-Net-based convolutional neural network for end-to-end segmentation of the liver from volumetric CT images. The model is trained and tested on the LiTS dataset using a standard preprocessing pipeline and an in-house composite loss function—AdvancedComboLoss—combining Dice Loss, Binary Cross-Entropy, and Focal Tversky Loss to balance class imbalance and boundary accuracy. The network handles entire 3D volumes with spatial context retained between anatomical planes and is trained on a GPU-enabled setup with the PyTorch environment. Objectives: To develop a highly accurate and computationally efficient 3D U-Net-based deep learning model for automated liver segmentation from volumetric CT scans, addressing challenges like class imbalance, anatomical variability, and low tissue contrast, using a custom loss function (AdvancedComboLoss) and a standardized preprocessing pipeline. Methods: The methodology adopted is volumetric liver segmentation from a specially designed 3D U-Net architecture specific to medical CT scans. The process entails preprocessing of raw CT scans into a common shape and intensity range and training a fully convolutional 3D neural network that learns the spatial relationships among all three anatomical planes. The model uses an encoder-decoder architecture with skip connections to maintain fine-grained structural information, allowing for accurate delineation of liver regions. A hybrid loss function—integrating Dice, Binary Cross-Entropy, and Focal Tversky losses—is used to direct the learning process to optimize overlap of regions, accuracy of classification, and class imbalance. Results: Experimental outcomes demonstrate that the model has a Dice score of 0.9604 and voxel-wise accuracy of 0.9979, indicating excellent performance in segmentation as well as strong agreement with expert annotations. The model presented in this paper presents a solid, scalable, and clinically valuable approach to automated liver segmentation with future potential to integrate into multi-stage liver and tumor analysis workflows. Conclusions: Herein, we built and tested a 3D U-Net deep learning architecture for unsupervised liver segmentation from abdominal CT volumes. Through utilization of volumetric convolutions as well as through a well-engineered composite loss function, the model was successful in capturing complicated liver anatomy as well as being able to hold high performance even on varied CT volumes. The power of the method is not just in its quantitative performance but also in its capability to generate anatomically consistent and visually plausible segmentations, making it extremely relevant to actual clinical workflows. Although there are some limitations—constrained batch size and absence of data augmentation—there is room for improvement, the findings affirm that deep learning can be an effective tool for automating key tasks in medical image analysis. As a whole, this paper adds a robust and effective segmentation model that would be potentially embedded in wider diagnostic workflows, ending up improving the accuracy, speed, and scalability of liver evaluation in clinical practice. In brief, this paper adds a high-fidelity, scalable, and computationally effective solution for computer-aided liver segmentation, pushing the application of artificial intelligence in clinical decision-making and medical image analysis.

Background: Automatic segmentation of liver regions as well as liver lesions such as hepatocellular carcinoma (HCC) from computed tomography (CT) images is critical for accurate diagnosis and therapy planning. With the advent of deep learning techniques such as transformers, computer-aided diagnostic tools (CADs) have the potential to increase the accuracy of liver tumor diagnosis, progression, and treatment planning. However, two major challenges remain: 1) existing models struggle to extract robust spatial features for accurate liver and liver lesion segmentation, and 2) publicly available liver datasets with HCC annotations are limited. Methods: We first present a new liver dataset acquired from Chongqing University Cancer Hospital (CCH-LHCC-CT) with HCC annotations. Second, we developed a novel deep learning architecture (ICT-Net), which is constructed based on a pretrained transformer encoder in conjunction with an advanced feature upscaling and enhanced convolution-transformer decoder formation. Results: We performed liver and liver tumor segmentation on the CCH-LHCC-CT and three public CT liver datasets. The proposed ICT-Net architecture achieves superior accuracy (higher ACC/DSC/IoU, lower HD95) across all datasets. Conclusions: We construct a novel deep-learning architecture that produces robust information for liver and liver tumor segmentation. The statistical and visual results demonstrate that the proposed ICT-Net outperforms other existing approaches investigated in this study in terms of ACC, DSC, and IoU. Clinical Translation Statement: ICT-Net enhances surgical planning accuracy through precise tumor margin delineation and improves therapy response assessment reliability, which holds meaningful promise to support more precise and effective clinical therapeutic strategies for patients with HCC.

Segmentation of focal liver lesions serves as an essential preprocessing step for initial diagnosis, stage differentiation, and post-treatment efficacy evaluation. Multimodal MRI scans (e.g., T1WI, T2WI) provide complementary information on liver lesions and is widely used for diagnosis. However, some modalities (e.g., T1WI) have high resolution but lack of important visual information (e.g., edge) belonged to other modalities (T2WI), it is significant to enhance tissue lesion quality in T1WI using other modality priors (T2WI) and improve segmentation performance. In this paper, we propose a graph learning based approach with the motivation of extracting modality-specific features efficiently and establishing the regional correspondence effectively between T1WI and T2WI. We first project deep features into a graph domain and employ graph convolution to propagate information across all regions for extraction of modality-specific features. Then we propose a mutual information based graph co-attention module to learn weight coefficients of one bipartite graph, which is constructed by the fully-connection of graphs with different modalities in the graph domain. At last, we get the final refined features for segmentation by re-projection and residual connection. We validate our method on a multimodal MRI liver lesion dataset. Experimental results show that the proposed approach achieves improvement of liver lesion segmentation in T1WI by learning guided features from multimodal priors (T2WI) compared to existing methods.

Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter-operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter-operator repeatability of 92.3%.

The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Retrospective. 395 patients with 1149 FLLs. The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Stage 2.

Deep Learning for Automated Liver Segmentation to Aid in the Study of Infectious Diseases in Nonhuman Primates

Deep learning-based diagnosis systems are useful to identify abnormalities in medical images with the greatly increased workload of doctors. Specifically, the rate of new cases and deaths from malignancies is rising for liver diseases. Early detection of liver lesions plays an extremely important role in effective treatment and gives a higher chance of survival for patients. Therefore, automatic detection and classification of common liver lesions are essential for doctors. In fact, radiologists mainly rely on Hounsfield Units to locate liver lesions but previous studies often pay little attention to this factor. In this paper, we propose an improved method for the automatic classification of common liver lesions based on deep learning techniques and the variation of Hounsfield Unit densities on CT images with and without contrast. Hounsfield Unit is used to locate liver lesions accurately and support data labeling for classification. We construct a multi-phase classification model developed on the deep neural networks of Faster R-CNN, R-FCN, SSD, and Mask R-CNN with the transfer learning approach. The experiments are conducted on six scenarios with multi-phase CT images of common liver lesions. Experimental results show that the proposed method improves the detection and classification of liver lesions compared with recent methods because its accuracy achieves up to 97.4%. The proposed models are very useful to assist doctors in the automatic segmentation and classification of liver lesions to solve the problem of depending on the clinician's experience in the diagnosis and treatment of liver lesions.

IntroductionLeveraging deep learning in the radiology community has great potential and practical significance. To explore the potential of fitting deep learning methods into the current Liver Imaging Reporting and Data System (LI-RADS) system, this paper provides a complete and fully automatic deep learning solution for the LI-RADS system and investigates its model performance in liver lesion segmentation and classification.MethodsTo achieve this, a deep learning study design process is formulated, including clinical problem formulation, corresponding deep learning task identification, data acquisition, data preprocessing, and algorithm validation. On top of segmentation, a UNet++-based segmentation approach with supervised learning was performed by using 33,078 raw images obtained from 111 patients, which are collected from 2010 to 2017. The key innovation is that the proposed framework introduces one more step called feature characterization before LI-RADS score classification in comparison to prior work. In this step, a feature characterization network with multi-task learning and joint training strategy was proposed, followed by an inference module to generate the final LI-RADS score.ResultsBoth liver segmentation and feature characterization models were evaluated, and comprehensive statistical analysis was conducted with detailed discussions. Median DICE of liver lesion segmentation was able to achieve 0.879. Based on different thresholds, recall changes within a range of 0.7 to 0.9, and precision always stays high greater than 0.9. Segmentation model performance was also evaluated on the patient level and lesion level, and the evaluation results of (precision, recall) on the patient level were much better at approximately (1, 0.9). Lesion classification was evaluated to have an overall accuracy of 76%, and most mis-classification cases happen in the neighboring categories, which is reasonable since it is naturally difficult to distinguish LI-RADS 4 from LI-RADS 5.DiscussionIn addition to investigating the performance of the proposed model itself, extensive comparison experiment was also conducted. This study shows that our proposed framework with feature characterization greatly improves the diagnostic performance which also validates the effectiveness of the added feature characterization step. Since this step could output the feature characterization results instead of simply generating a final score, it is able to unbox the black-box for the proposed algorithm thus improves the explainability.

To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs). This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material-enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen κ, Bland-Altman analyses, and analysis of variance. In the test cohort, for lesion size smaller than 10 mm (n = 30), 10-20 mm (n = 35), and larger than 20 mm (n = 40), the detection sensitivity of the automated method was 10%, 71%, and 85%; positive predictive value was 25%, 83%, and 94%; Dice similarity coefficient was 0.14, 0.53, and 0.68; maximum symmetric surface distance was 5.2, 6.0, and 10.4 mm; and average symmetric surface distance was 2.7, 1.7, and 2.8 mm, respectively. For manual and user-corrected segmentation, κ values were 0.42 (95% confidence interval: 0.24, 0.63) and 0.52 (95% confidence interval: 0.36, 0.72); normalized interreader agreement for lesion volume was -0.10 ± 0.07 (95% confidence interval) and -0.10 ± 0.08; and mean interaction time was 7.7 minutes ± 2.4 (standard deviation) and 4.8 minutes ± 2.1 (P < .001), respectively. Automated detection and segmentation of CLM by using deep learning with convolutional neural networks, when manually corrected, improved efficiency but did not substantially change agreement on volumetric measurements.© RSNA, 2019Supplemental material is available for this article.

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

Medical image segmentation is a fundamental task in medical image analysis, playing a crucial role in disease diagnosis, treatment planning, and clinical decision-making. Accurate segmentation of anatomical structures, such as blood vessels, organs, and lesions, is essential for reliable medical interpretations. However, existing segmentation models often face challenges in effectively capturing both local and global features within medical images. Traditional convolutional neural networks (CNNs) excel at extracting fine-grained local features but struggle to model long-range dependencies due to their limited receptive fields. Conversely, transformer-based models can capture global contextual relationships but often fail to preserve detailed local structures, leading to suboptimal segmentation performance. The purpose of this study is to develop SEFormer, a novel hybrid network that integrates SENet, ResNet, and Transformer to enhance medical image segmentation. SEFormer aims to effectively capture both local and global features, addressing the limitations of traditional CNNs and transformer-based models.SENet is incorporated to recalibrate feature maps by adaptively emphasizing informative features, thereby compensating for the global representation limitations of CNNs. Meanwhile, the ResNet backbone ensures deep feature extraction while maintaining computational efficiency. Additionally, the Transformer module is introduced to capture long-range dependencies and enhance contextual awareness, complementing the local feature extraction capabilities of CNNs. This hybrid approach allows SEFormer to balance fine-grained spatial details with broader contextual information, leading to more precise segmentation results.Furthermore, to mitigate information loss during feature extraction and ensure a more complete and hierarchical representation of image information, we introduce a feature pyramid structure inspired by multi-scale image pyramid models. By progressively increasing the receptive field across different scales and employing skip connections, SEFormer effectively fuses multi-scale local and global features at each stage of the pyramid. This hierarchical fusion mechanism ensures a richer and more robust feature representation, which is particularly beneficial for segmenting complex medical structures such as blood vessels. We evaluate SEFormer on the CHASE_DB1 dataset, a widely used benchmark for retinal vessel segmentation. Experimental results demonstrate that SEFormer outperforms existing state-of-the-art segmentation methods, achieving a 3.25% improvement in segmentation accuracy. Additionally, we conduct ablation studies to verify the contributions of different components within SEFormer. The results show that incorporating SENet enhances feature recalibration and channel attention, while the Transformer module significantly improves global context awareness. The feature pyramid structure further contributes to performance gains by ensuring a multi-scale representation of vascular structures. Compared to conventional CNN-based methods, our approach achieves better segmentation accuracy with improved robustness to variations in vessel thickness, noise, and image contrast. Furthermore, SEFormer maintains computational efficiency, making it suitable for real-world clinical applications where both accuracy and processing speed are critical. SEFormer provides an efficient and accurate approach for medical image segmentation by leveraging the complementary strengths of CNNs, SENet, and Transformers while integrating a feature pyramid structure to maximize feature representation. Future research directions include extending SEFormer to other medical imaging modalities, such as CT and MRI scans, as well as exploring further optimization techniques to reduce computational costs while maintaining high segmentation performance. Additionally, integrating SEFormer with active learning frameworks could further improve segmentation performance by leveraging expert-annotated data more efficiently.

Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver segmentation in CT scan is important for diagnosis and analysis of liver lesions. However, the accuracy of existing liver segmentation methods is slightly insufficient. In this paper, we propose a liver segmentation architecture named CR-U-Net, which is composed of cascade U-Net combined with residual mapping. We make use of the MDice loss function for training in CR-U-Net, and the second-level of cascade network is deeper than the first-level to extract more detailed image features. Morphological algorithms are utilized as an intermediate-processing step to improve the segmentation accuracy. In addition, we evaluate our proposed CR-U-Net on liver segmentation task under the dataset provided by the 2017 ISBI LiTS Challenge. The experimental result demonstrates that our proposed CR-U-Net can outperform the state-of-the-art methods in term of the performance measures, such as Dice score, VOE, and so on.

Liver lesion segmentation from abdomen computed tomography (CT) with deep neural networks remains challenging due to the small volume and the unclear boundary. To effectively tackle these problems, in this paper, we propose a cascaded deeply supervised convolutional networks (CDS-Net). The cascaded deep supervision (CDS) mechanism uses auxiliary losses to construct a cascaded segmentation method in a single network, focusing the network attention on pixels that are more difficult to classify, so that the network can segment the lesion more effectively. CDS mechanism can be easily integrated into standard CNN models and it helps to increase the model sensitivity and prediction accuracy. Based on CDS mechanism, we propose a cascaded deep supervised ResUNet, which is an end-to-end liver lesion segmentation network. We conduct experiments on LiTS and 3DIRCADb dataset. Our method has achieved competitive results compared with other state-of-the-art ones.

Automated segmentation of liver and lesion in contrast-enhanced abdominal computed tomography (CT) scans is a crucial step for computer-assisted diagnosis and surgical planning of liver diseases. Although deep convolutional neural networks (DCNNs) have contributed many breakthroughs in liver and lesion segmentation task from CT images, the problem remains challenging due to low segmentation accuracy and high complexity. To address these issues, we proposed a light-weight convolutional neural networks for liver and lesion segmentation from CT images, i.e., LW-Mnet. Firstly, we improve the basic module of MobileNetv3 as the backbone of LW-Mnet. The second is that we employ LWASPP to capture multi-scale feature information for enhance representation power of network. The proposed network is evaluated on the public LiTS dataset, and the results show that LW-Mnet achieved the better performance than other networks with fewer parameters and faster inference speed.

PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder