Multi-Organ Segmentation

Latest papers

15 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
Oct 21, 2026Derong Yu, Guoyan ZhengZero-Shot Medical Image Segmentation Domain Generalization for Semantic Segmentation
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No.800 Dongchuan Road, Shanghai, 200240, China
Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset
Sep 29, 2026Pedro R. A. S. Bassi, Wenxuan Li, Szymon Plotka +10Medical Image Segmentation Computed Tomography
Johns Hopkins University · Harvard Medical School · Massachusetts General Hospital +5
FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation
Sep 21, 2026Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs ElschotMulti-Organ Segmentation Multi-Task Learning
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway · Central Staff at St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation
Sep 15, 2026Dakini Mallam Garba, Salim Abdou DaouraMulti-Organ Segmentation Medical Image Segmentation
ML Collective
ThreshGuide: Class-Aware Labeled-Guided Thresholding for Semi-Supervised 3D Abdominal Multi-Organ Segmentation
Sep 14, 2026Hongyu Liu, Yinlong Wang, Lusha Li +1Semi-Supervised Medical Image Segmentation Medical Image Segmentation
School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift
Aug 18, 2026Souraj Adhikary, Negar Chabi, Andre MastmeyerMulti-Organ Segmentation Medical Image Segmentation
Department of Engineering Sciences, Jade Hochschule Wilhelmshaven, Germany
Inference-Time Orthogonal Seeding Enables Geometry-Aligned 3D Organ Segmentation for Slice-Propagation Methods
Aug 12, 2026Md Rakibul Haque, Tushar Kataria, Shireen Y. ElhabianMulti-Organ Segmentation Medical Image Segmentation
Kahlert School of Computing, University of Utah, Salt Lake City, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation
Aug 8, 2026Satvik Praveen, Shengji Jin, Ahmed Lamidi +2Ultrasound Image Segmentation Promptable Image Segmentation
Computer Science & Engineering, University of South Florida, Tampa, USA · Department of Physics and Astronomy, Stony Brook University, Stony Brook, USA
Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation
Jul 31, 2026Priya Tomar, Aditya Parikh, Christian Bauckhage +1Multi-Organ Segmentation Cross-Domain Transfer Learning
Fraunhofer IAIS · University of Bonn, Germany
Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps
Jun 5, 2026Daria Kern, Negar Chabi, Souraj Adhikary +1Multi-Organ Segmentation U-Net
Glasgow Caledonian University School of Science & Engineering Glasgow G4 0BA, United Kingdom · Jade University of Applied Sciences Department of Engineering & Medical Technology Germany, 26389, Wilhelmshaven, Germany
GPAFormer: Graph-guided Patch Aggregation Transformer for Efficient 3D Medical Image Segmentation
Apr 8, 2026Chung-Ming Lo, I-Yun Liu, Wei-Yang LinMulti-Organ Segmentation Medical Image Segmentation
Institute of Artificial Intelligence Innovation, Industry Academia Innovation School, National Yang Ming Chiao Tung University · Department of Computer Science and Information Engineering, National Chung Cheng University
MD-RWKV-UNet: Scale-Aware Anatomical Encoding with Cross-Stage Fusion for Multi-Organ Segmentation
Mar 28, 2026Zhuoyi FangMulti-Organ Segmentation Medical Image Segmentation
School of Mathematical Sciences Yangzhou University
CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation
Mar 20, 2026Yuyang Zheng, Mingda Zhang, Jianglong Qin +4Medical Image Segmentation Multi-Organ Segmentation
School of Software Engineering, Yunnan University, KunMing, 650500, Yunnan, China. · The School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, China. · Yunnan Provincial Key Laboratory of Software Engineering, Yunnan University, Kunming, 650500, Yunnan, China.
SegMate: Asymmetric Attention-Based Lightweight Architecture for Efficient Multi-Organ Segmentation
Feb 27, 2026Andrei-Alexandru Bunea, Dan-Matei Popovici, Radu Tudor IonescuMulti-Organ Segmentation Medical Image Segmentation
POLITEHNICA Bucharest · University of Bucharest
SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation
Sep 21, 2025Yingzhen Hu, Yiheng Zhong, Aijing Yang +2Medical Image Segmentation Segment Anything Models
Mohamed bin Zayed University of AI, Abu Dhabi, UAE · Xi’an Jiaotong-Liverpool University, Suzhou, China · University of Liverpool, Liverpool, United Kingdom

Oct 21, 2026J/K move · Enter open · S save

ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning

Derong Yu, Guoyan Zheng

Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No.800 Dongchuan Road, Shanghai, 200240, China

Zero-Shot Medical Image Segmentation Domain Generalization for Semantic Segmentation Multi-Organ Segmentation Medical Image Representation Learning Domain Randomization

Open paper AskPDF arXiv

Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, their heavy reliance on large-scale image data raises concerns over generalization capability and privacy risks. This motivates the development of generalizable segmentation training paradigms based on domain-randomized image synthesis, facilitating generalization across unseen real image domains. However, existing paradigms either suffer from limited segmentation performance when applied to abdominal organs, or require dataset-specific finetuning. To address these limitations, we propose ReSeg, a novel two-stage retraining-free framework for domain-agnostic zero-shot abdominal organ segmentation. At the first stage, our organ-agnostic synthetic data-driven pretraining approach exploits semantically and visually diverse synthetic images generated by a dedicated data engine to optimize a Generalizable Semantic Representation Network (GSRNet). The network is thereby capable of learning semantic features robust against variations in anatomical structure, imaging contrast, resolution and spatial context, which provide universal representation capabilities that serve as a foundation for segmentation learning. Subsequently, in the second stage, we continue to train an Organ-specific Encoder-decoder Segmentation Network (OESNet), whose encoder is initialized with the pretrained weights. This training process is facilitated by another dedicated data engine, which generates diversified visual images while strictly retaining the inherent spatial layout of abdominal organs. Comprehensive evaluation on seven abdominal organ segmentation datasets across three different modalities including CT, MRI, and PET demonstrates that ReSeg consistently outperforms existing state-of-the-art (SOTA) methods while eliminating the need for real training images.