3D Medical Image Segmentation

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18 papers in the last four weeks, up 260% on the four weeks before. 0.2% of all new papers.

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CRIL-U-Net: Compact Ratio-Interaction Learning for Focal Cortical Dysplasia Segmentation from T1w and FLAIR MRI
Aug 4, 2026Soumen Ghosh, Amit Soni Arya, Tilottama Goswami +3Cross-Modality Medical Image Segmentation Cross-Modal Representation Learning
Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided
Aug 1, 2026Ling Ren, Chao Deng, Ziming Wang +2Source-Free Domain Adaptation Test-Time Adaptation
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China · Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583 · State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases, the Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing 210029, China
RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
Aug 1, 2026Kai Geissler, Laurens Müller-Groh, Hans Meine3D Medical Imaging Efficient Object Detection
Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany
MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
Jul 31, 2026John Garcia Henao, Nicholas Bünger, Benedikt Herzog +11Medical Imaging Foundation Models Inference-Time Optimization
Digital Medicine Unit Team, Balgrist University Hospital, 8008 Zurich, Switzerland · Research Centre for Computational Health, Zurich University of Applied Sciences (ZHAW), 8820 Wädenswil, Switzerland · Hip and Pelvis Surgery Team, Balgrist University Hospital, 8008 Zurich, Switzerland +1
SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting
Jul 31, 2026Yu Song, Hao Sun, Shiyu Teng +2Image Segmentation Vision Foundation Model Adaptation
College of Information Science and Engineering, Ritsumeikan University 2-150 Iwakura-cho, Ibaraki, Osaka 567-8570, Japan
A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging
Jul 30, 2026Diego J. Torrejón, Luna Y. Hernández, Javier Sánchez3D ViTs 3D Medical Image Segmentation
Centro de Tecnologías de la Imagen (CTIM) Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES) University of Las Palmas de Gran Canaria 35017 Las Palmas de Gran Canaria, Spain · Centro de Tecnologías de la Imagen (CTIM) Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES) University of Las Palmas de Gran Canaria2026 35017 Las Palmas de Gran Canaria, Spain
AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans
Jul 30, 2026Jingwen Yang, Senmao Wang, Luoyao Kang +6World Model Learning Action-Conditioned World Models
Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100144, China · The Chinese University of Hong Kong, Hong Kong SAR, 999077, China · The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China
BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens
Jul 29, 2026David Hagerman, Roman Naeem, Fredrik KahlImage Segmentation 3D Medical Image Segmentation
Chalmers University of Technology, 412 96 Gothenburg, Sweden
SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images
Jul 24, 2026Baptiste Podvin, Alexandre Ancel, Flavio Milana +8Interactive Medical Image Segmentation Promptable Image Segmentation
IRCAD France, Strasbourg, France · Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Milan, Italy · Department of Biomedical Sciences, Humanitas University, Milan, Italy +2
Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention
Jul 23, 2026Alexandru Crişan, Diana BorzaDomain Generalization 3D Medical Image Segmentation
Babes-Bolyai University, Cluj-Napoca, Romania
A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability
Jul 22, 2026Oliver Mills, Philip Conaghan, Samuel ReltonMedical Image Analysis OOD Generalization
University of Leeds, Leeds, UK · NIHR Leeds Biomedical Research Centre, Leeds, UK
StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows
Jul 22, 2026Youwan Mahé, Axel Plessis, Stéphanie Leplaideur +3Efficient Neural Network Inference Medical Image Segmentation
EMPENN, MALT · Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn, Rennes, France · Siemens Healthineers, Courbevoie, France +6
DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentation
Jul 21, 2026Mohammad Arafat Hussain, Ellen Grant, Yangming OuMamba U-Net
Boston Children’s Hospital, Harvard Medical School
Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography
Jul 21, 2026Andreas W. Aspe, Jonas Jalili Loft, Michael Huy Cuong Pham +6Medical Image Segmentation 3D Medical Image Segmentation
DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark · Department of Cardiology, The Heart Center, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark · Department of Radiology, The Diagnostic Center, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark +2
SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens
Jul 20, 2026Zhao Wang, Wei Dai, Hongfu Sun +2Medical Imaging Foundation Models 3D Medical Image Segmentation
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia · School of Engineering, College of Engineering, Science and Environment, University of Newcastle, Australia · School of Human Movement and Nutrition Sciences, The University of Queensland, Australia
PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations
Jul 20, 2026Tsubasa Konno, Takahiro Ninomiya, Yukun Zhou +6Multi-View Consistency Optical Coherence Tomography
Graduate School of Information Sciences, Tohoku University, Japan · Department of Ophthalmology, Graduate School of Medicine, Tohoku University · Institute of Ophthalmology, University College London, UK +3
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Jul 14, 2026Yunzhou Li, Jiesi Hu, Yanwu Yang +5Interactive Medical Image Segmentation Medical Imaging Foundation Models
Harbin Institute of Technology at Shenzhen, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China · University Hospital Tübingen, Tübingen, Germany +2
VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
Jul 8, 2026Zimu Zhang, Yiheng Zhong, Zhuoru Zhang +4Prototype Learning Semi-Supervised Medical Image Segmentation
2Xi’an Jiaotong-Liverpool University, China · 1Yale University, United States
Towards Accurate and Fast Clinical Body Composition: A Resource-Efficient Hierarchical Segmentation Framework for Multi-Source CT
Jul 8, 2026Xiaodi Shen, Qingzhu Zheng, Yaoyang Qiu +12Medical Image Analysis Medical Image Segmentation
Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China · Research & Development Center, Canon Medical Systems (China) Co. Ltd. Beijing 100015, China
TRACE-Seg3D: Counterfactual Context Auditing For Robust 3D Glioma Segmentation Under Institutional Shift
Jul 8, 2026Nguyen Linh Dan Le, Nguyen Pham Hoang Le, Tran Dang KhoiDomain Generalization 3D Medical Image Segmentation
The University of Melbourne, Melbourne, Australia · University of Information Technology, Vietnam National University, Vietnam · Industrial University of Ho Chi Minh City, Vietnam
Displacement Preserving Relational Distillation for Robust Medical Segmentation
Jul 6, 2026Zhicheng Ding, Xinyu Chu, Jung Im Choi +5Model Compression Relational Knowledge Distillation
Bowling Green State University, Bowling Green, OH, USA · University of Alabama at Birmingham, Birmingham, AL, USA · The University of Findlay, Findlay, OH, USA +3
Population-Scale Segmentation of Penile Tissue in DIXON MRI using Deep Learning for Quantitative Phenotyping in Male Reproductive Health
Jul 2, 2026Jan Ernsting, Gunnar Paul Kordes, Nils Johannaber +5Healthcare Magnetic Resonance Imaging
University of Münster Germany
Towards Voxel Spacing Consistency for Medical Image Segmentation
Jun 30, 2026Xin You, Runze Yang, Minghui Zhang +63D Medical Image Segmentation
LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation
Jun 29, 2026Sebastian Aas, Elias Stenhede, Arian RanjbarPromptable Image Segmentation Medical Image Segmentation
Medical Technology & E-health, Akershus University Hospital, Lørenskog, Norway · Faculty of Medicine, University of Oslo, Oslo, Norway
MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network
Jun 23, 2026Fuyou Mao, Yifei Chen, Beining Wu +9Vision Foundation Model Adaptation Topology-Preserving Image Segmentation
Central South University, 410083 Changsha, China · Tsinghua University, 100084 Beijing, China · Hangzhou Dianzi University, 310018 Hangzhou, China +1
From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation
Jun 20, 2026Khoa Pham, Sindhuja Penchala, Jiacheng Li +2Retinal Vessel Segmentation Hybrid CNN-Transformer Architectures
Mississippi State University · The University of Alabama
Native space based pipelines outperform template space based pipeline in subcortical segmentation
Jun 19, 2026Tomás Lima, Daniel Novák, Eduard BakšteinDomain Adaptation Medical Image Segmentation
Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic · National Institute of Mental Health, Klecany, Czech Republic
Anatomically Consistent TMJ Disc Segmentation via Semantic Anchoring and Clinical Priors
Jun 19, 2026Dayun Ju, Chanyoung Kim, Sunyoung Jung +4Medical Image Segmentation 3D Medical Image Segmentation
Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea · Department of Computer Science, Emory University, Atlanta, Georgia, USA · College of Dentistry, Yonsei University, Seoul, Republic of Korea
Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentation
Jun 18, 2026Rémi Hattat, Marine Beaumont, Charline Bertholdt +3Active Learning 3D Medical Image Segmentation
IADI (U1254), Inserm and Université de Lorraine, Nancy, France · CIC-IT 1433, Inserm, Université de Lorraine, and CHRU Nancy, Nancy, France
CSWinUNETR: Segmentation of Thin Anatomical Structures in Medical Images
Jun 18, 2026Junho Moon, Haejun Chung, Ikbeom JangHybrid CNN-Transformer Architectures Efficient Attention
Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea

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CRIL-U-Net: Compact Ratio-Interaction Learning for Focal Cortical Dysplasia Segmentation from T1w and FLAIR MRI

Soumen Ghosh, Amit Soni Arya, Tilottama Goswami, Subhojit Mandal+2

Cross-Modality Medical Image Segmentation Cross-Modal Representation Learning U-Net Medical Image Segmentation 3D Medical Image Segmentation

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Focal cortical dysplasia (FCD) type II is an important structural cause of drug-resistant focal epilepsy, but its small size, heterogeneous appearance, and subtle MRI characteristics make automated segmentation challenging. Conventional multimodal networks commonly concatenate T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) images, requiring subsequent layers to learn useful cross-modal relationships implicitly. We propose CRIL-U-Net, a 3D U-Net incorporating a Compact Ratio-Interaction Learning module that combines local spatial features, voxel-wise cross-modal mixing, and bidirectional ratio-inspired interactions. CRIL-U-Net was compared with a conventional 3D U-Net and an input self-attention U-Net using five-fold cross-validation on 85 FCD subjects and 25 healthy controls. Each architecture was trained independently using Dice-binary cross-entropy (Dice-BCE) and Focal Tversky-Focal (FTF) losses. With FTF, CRIL-U-Net achieved the highest mean Dice score (0.196 +/- 0.262), compared with 0.136 +/- 0.224 for the U-Net and 0.135 +/- 0.214 for the attention comparator. It produced nonzero lesion overlap in 44 of 85 cases, compared with 36 for the U-Net. Under FTF, CRIL-U-Net significantly outperformed both comparison architectures after false-discovery-rate correction. These findings suggest that compact cross-modal representation learning can improve FCD segmentation within a controlled U-Net setting when combined with an imbalance-aware objective, although the remaining zero-overlap rate of 48.2% highlights the need for further validation and methodological development.

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