Liver Segmentation
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Benchmarks
LiTS2017
Most implemented
Med3D: Transfer Learning for 3D Medical Image Analysis
The Liver Tumor Segmentation Benchmark (LiTS)
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
Papers
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 Segmentation