Papers Liver Segmentation
“Liver Segmentation” 태그가 달린 논문 99편 · 필터 해제
Unsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentation
Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compounding challenges: the scarcity of annotated CBCT data and the large d…
Unsupervised Domain AdaptationLiver SegmentationUnsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global anatomical structures, …
Medical Image SegmentationLiver SegmentationA Unified Framework for the Detection and Classification of Fatty Pancreas in Ultrasound Images
Non-alcoholic fatty pancreas disease (NAFPD) is an underdiagnosed condition associated with metabolic syndrome, insulin resistance, and increased risk of pancreatic cancer. Diagnosis typically relies on subjective visual…
Feature EngineeringLiver SegmentationTransfer LearningUnsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy
In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally invasive procedures. CBCT differs from traditional Computed Tomograph…
Unsupervised Domain AdaptationLiver SegmentationVasGuideNet: Vascular Topology-Guided Couinaud Liver Segmentation with Structural Contrastive Loss
Accurate Couinaud liver segmentation is critical for preoperative surgical planning and tumor localization.However, existing methods primarily rely on image intensity and spatial location cues, without explicitly modelin…
Liver SegmentationSemi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI
Liver fibrosis poses a substantial challenge in clinical practice, emphasizing the necessity for precise liver segmentation and accurate disease staging. Based on the CARE Liver 2025 Track 4 Challenge, this study introdu…
Image SegmentationLiver SegmentationSSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2
Despite the success of deep learning based models in medical image segmentation, most state-of-the-art (SOTA) methods perform fully-supervised learning, which commonly rely on large scale annotated training datasets. How…
Semi-supervised Medical Image SegmentationLiver SegmentationFew-Shot LearningLiver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method
Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quantification and Analysis) dataset, establ…
Liver SegmentationLabel-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed across imaging modalities and vendor systems. We propose a label-efficient …
Liver SegmentationMulti-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images
Liver fibrosis represents the accumulation of excessive extracellular matrix caused by sustained hepatic injury. It disrupts normal lobular architecture and function, increasing the chances of cirrhosis and liver failure…
Liver SegmentationSRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Liver cirrhosis plays a critical role in the prognosis of chronic liver disease. Early detection and timely intervention are essential for reducing mortality rates. However, the intricate anatomical architecture and dive…
Liver SegmentationUncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation
Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during training and validation. However, in clinical settings without manual a…
Image SegmentationLiver SegmentationT-MPEDNet: Unveiling the Synergy of Transformer-aware Multiscale Progressive Encoder-Decoder Network with Feature Recalibration for Tumor and Liver Segmentation
Precise and automated segmentation of the liver and its tumor within CT scans plays a pivotal role in swift diagnosis and the development of optimal treatment plans for individuals with liver diseases and malignancies. H…
Tumor SegmentationLiver SegmentationAttention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images
Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is essential for understanding body composition and associated health risk…
Computational EfficiencyLiver SegmentationSegmentationCross Modality Medical Image Synthesis for Improving Liver Segmentation
Deep learning-based computer-aided diagnosis (CAD) of medical images requires large datasets. However, the lack of large publicly available labeled datasets limits the development of deep learning-based CAD systems. Gene…
Image GenerationLiver SegmentationA Reverse Mamba Attention Network for Pathological Liver Segmentation
We present RMA-Mamba, a novel architecture that advances the capabilities of vision state space models through a specialized reverse mamba attention module (RMA). The key innovation lies in RMA-Mamba's ability to capture…
Computational EfficiencyLiver SegmentationMambaSegmentation+2Registration of Longitudinal Liver Examinations for Tumor Progress Assessment
Assessing cancer progression in liver CT scans is a clinical challenge, requiring a comparison of scans at different times for the same patient. Practitioners must identify existing tumors, compare them with prior exams,…
Liver SegmentationProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation
The growing need for accurate and efficient 3D identification of tumors, particularly in liver segmentation, has spurred considerable research into deep learning models. While many existing architectures offer strong per…
Image SegmentationKolmogorov-Arnold NetworksLiver SegmentationMedical Image Segmentation+1SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing
Liver cancer is a leading cause of mortality worldwide, and accurate Computed Tomography (CT)-based tumor segmentation is essential for diagnosis and treatment. Manual delineation is time-intensive, prone to variability,…
Clinical KnowledgeComputed Tomography (CT)Liver SegmentationSegmentation+1Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature inte…
Liver SegmentationSegmentation