Medical Image Segmentation

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Overview

Medical image segmentation assigns labels to pixels or voxels to outline structures such as tumors, organs, vessels, and surgical instruments, enabling quantitative analysis and clinical workflows. Common approaches include U-Net-style convolutional networks, transformers and state-space models, and promptable foundation models; work also uses anatomical and topological priors, text guidance, and generative models. To reduce reliance on dense expert labels, researchers study self-supervised pretraining, semi- and weakly supervised learning, synthetic data, and interactive annotation, while domain adaptation addresses differences across scanners, hospitals, and modalities. A central challenge is making models reliable beyond their training data: high overlap scores can conceal boundary, connectivity, small-lesion, or subgroup failures, so external validation, uncertainty estimates, and clinically relevant metrics remain important.

Latest papers

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786 papers62 papers in the last four weeks, down 29% on the four weeks before. 0.4% of all new papers.

  1. A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

    Apr 22, 2024Ming Kang, Fung Fung Ting, Shier Nee Saw +3Cross-Modal Knowledge DistillationCross-Modality Medical Image Segmentation

    School of Information Technology, Monash University, Malaysia Campus, Subang Jaya 47500, Malaysia · Augmented Intelligence and Multimodal analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia · Monash Medical AI Group, Monash University, Clayton VIC 3800, Australia +1

  2. The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

    Dec 29, 2023Kaiyuan Yang, Fabio Musio, Yihui Ma +112Image SegmentationMedical Image Segmentation

  3. Full-resolution MLPs Empower Medical Dense Prediction

    Nov 28, 2023Mingyuan Meng, Yuxin Xue, Dagan Feng +2Medical Image RegistrationMedical Image Segmentation

    School of Computer Science, The University of Sydney, Australia. · Institute of Translational Medicine, Shanghai Jiao Tong University, China.

  4. Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

    Apr 20, 2022Changjie Lu, Shen Zheng, Gaurav GuptaUnsupervised Domain AdaptationMedical Image Segmentation

    College of Science and Technology, Wenzhou-Kean University, Wenzhou, China

  5. Research on Deep Learning-Based Semantic Segmentation Algorithms for Subcortical Brain Structures

    Jul 22, 2019Binbin Yang, Weiwei ZhangMedical Image SegmentationConvolutional Neural Networks

    Peking Union Medical College, Tsinghua University, Beijing, China

  6. Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

    Jul 11, 2013Quan Wang, Dijia Wu, Le Lu +3Medical Image Segmentation3D Medical Image Segmentation

    Siemens Corporate Research, Princeton, NJ 08540, USA · Rensselaer Polytechnic Institute, Troy, NY 12180, USA