Cross-Modality Medical Image Segmentation

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The field develops methods to segment organs, lesions, and other structures when images come from different modalities, such as MRI, CT, PET, and ultrasound, or when some modalities are missing. Common approaches include unsupervised domain adaptation, image synthesis, multimodal feature fusion, self-supervised pretraining, and anatomical shape or topology priors. Models are tested on settings such as brain-tumor MRI, cardiac MRI-to-CT transfer, and PET/CT lesion segmentation, often with scarce labels or shifts between hospitals. A central challenge is that complementary modalities can improve predictions, but misalignment, noise, or conflicting signals can make fusion worse than a strong single-modality model, so external validation and modality-specific comparisons remain important.

Latest papers

72 papers5 papers in the last four weeks, down 44% on the four weeks before. 0.0% of all new papers.

Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation
Mar 13, 2026Fares Bougourzi, Fadi Dornaika, Abdenour HadidMedical Image Segmentation Cross-Modality Medical Image Segmentation
IEMN UMR CNRS 8520, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France · University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain · Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi, Abu Dhabi, UAE
Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy
Mar 10, 2026Gauthier Miralles, Loïc Le Folgoc, Vincent Jugnon +1Unsupervised Domain Adaptation Cross-Modality Medical Image Segmentation
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France · GE Healthcare, Buc, France
TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomy
Mar 10, 2026Yaoyu Liu, Minghui Zhang, Xin You +2Medical VLMs Topology-Preserving Image Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China · Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
Feb 24, 2026Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1Cross-Modality Medical Image Segmentation Data Augmentation
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway. · Central Staff, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
Uncertainty-Aware Vision-Language Segmentation for Medical Imaging
Feb 16, 2026Aryan Das, Tanishq Rachamalla, Koushik Biswas +2Cross-Modality Medical Image Segmentation Medical VLMs
VIT Bhopal · SAHE, Andhra Pradesh · IIIT Delhi +2
Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA
Sep 26, 2025Chen Li, Meilong Xu, Xiaoling Hu +2Cross-Modality Medical Image Segmentation Unsupervised Domain Adaptation
Stony Brook University, Stony Brook, NY, USA · Massachusetts General Hospital and Harvard Medical School, MA, USA
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
Sep 26, 2025Jinpeng Lu, Linghan Cai, Yinda Chen +4Cross-Modality Medical Image Segmentation Image Segmentation
University of Science and Technology of China · Dresden University of Technology · Harbin Institute of Technology, Shenzhen
Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer
Aug 15, 2025Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. RajapakseCross-Modality Medical Image Segmentation Medical Image Segmentation
College of Computing and Data Science, Nanyang Technological University, Singapore
Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation
May 17, 2025Pengfei Lyu, Pak-Hei Yeung, Jing Xia +5Cross-Modal Learning Cross-Modality Medical Image Segmentation
Northeastern University, Shenyang, China · Nanyang Technological University, Singapore
Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation
Nov 11, 2024Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +10Domain Randomization Cross-Modality Medical Image Segmentation
Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland · CIBM Center for Biomedical Imaging, Lausanne, Switzerland · Institute for Information Processing, Leibniz University Hannover, Hannover, Germany +5
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 Distillation Cross-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
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
Apr 20, 2022Changjie Lu, Shen Zheng, Gaurav GuptaUnsupervised Domain Adaptation Medical Image Segmentation
College of Science and Technology, Wenzhou-Kean University, Wenzhou, China

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Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation

Fares Bougourzi, Fadi Dornaika, Abdenour Hadid

IEMN UMR CNRS 8520, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France · University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain · Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi, Abu Dhabi, UAE

Medical Image Segmentation Cross-Modality Medical Image Segmentation Visual SSMs

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Deep learning has achieved remarkable success in medical image segmentation, often reaching expert-level accuracy in delineating tumors and tissues. However, most existing approaches remain task-specific, showing strong performance on individual datasets but limited generalization across diverse imaging modalities. Moreover, many methods focus primarily on the encoder, relying on large pretrained backbones that increase computational complexity. In this paper, we propose a decoder-centric approach for generalized 2D medical image segmentation. The proposed Deco-Mamba follows a U-Net-like structure with a Transformer-CNN-Mamba design. The encoder combines a CNN block and Transformer backbone for efficient feature extraction, while the decoder integrates our novel Co-Attention Gate (CAG), Vision State Space Module (VSSM), and deformable convolutional refinement block to enhance multi-scale contextual representation. Additionally, a windowed distribution-aware KL-divergence loss is introduced for deep supervision across multiple decoding stages. Extensive experiments on diverse medical image segmentation benchmarks yield state-of-the-art performance and strong generalization capability while maintaining moderate model complexity. The source code will be released upon acceptance.