3D Medical Image Segmentation

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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
SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Aug 17, 2025Jun Zeng, Quoc-Huy Trinh, Deepak Ranjan Nayak +3Mamba Medical Image Segmentation
Chongqing University of Posts and Telecommunications, China · Aalto University, Finland · Malaviya National Institute of Technology Jaipur, India +2
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Date pendingBehraj Khan, Tahir Qasim Syed, Syed Ahmad Chan BukhariImage Segmentation Promptable Image Segmentation
St. John’s University New York, USA · Institute of Business Administration Karachi Pakistan
MCSeg: Pre-training and Fine-tuning Volumetric Pyramid Transformer for Multi-modal Cardiac Image Segmentation
Date pendingZhiyu Ye, Hairong Zheng, Tong ZhangCross-Modality Medical Image Segmentation Hybrid CNN-Transformer Architectures
State Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · University of Chinese Academy of Sciences, Beijing, China
Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation
Date pendingLibing Kuang, Soren Salehi, Ziling Wu +2Medical Image Segmentation 3D Medical Image Segmentation
School of Computer Science, University of Nottingham, Nottingham, UK · Electrical Engineering Department, Sharif University of Technology, Tehran, Iran · University of Pittsburgh, Pittsburgh, PA, USA

Sep 26, 2025, cs.CVJ/K move · Enter open · S save

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

Jinpeng Lu, Linghan Cai, Yinda Chen, Guo Tang+3

University of Science and Technology of China · Dresden University of Technology · Harbin Institute of Technology, Shenzhen

Cross-Modality Medical Image Segmentation Image Segmentation Hybrid CNN-Transformer Architectures 3D Medical Image Segmentation

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Lightweight 3D medical image segmentation remains constrained by a fundamental \textit{

efficiency / robustness conflict''}, particularly when processing complex anatomical structures and heterogeneous modalities. In this paper, we study how to redesign the framework based on the characteristics of high-dimensional 3D images, and explore data synergy to overcome the fragile representation of lightweight methods. Our approach, VeloxSeg, begins with a deployable and extensible dual-stream CNN-Transformer architecture composed of Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC). For each 3D image, we invoke a

glance-and-focus'' principle, where PWA rapidly retrieves multi-scale information, and JLC ensures robust local feature extraction with minimal parameters, significantly enhancing the model's ability to operate with low computational budget. Followed by an extension of the dual-stream architecture that incorporates modal interaction into the multi-scale image-retrieval process, VeloxSeg efficiently models heterogeneous modalities. Finally, Spatially Decoupled Knowledge Transfer (SDKT) via Gram matrices injects the texture prior extracted by a self-supervised network into the segmentation network, yielding stronger representations than baselines at no extra inference cost. Experimental results on multimodal benchmarks show that VeloxSeg achieves a 26% Dice improvement, alongside increasing GPU throughput by 11

\times

, CPU by 48

\times

, and reducing training peak GPU memory usage by

1/20

, inference by

1/24

. Code is available at https://github.com/JinPLu/VeloxSeg.

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Related topics

Latest papers 125

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes

Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

MCSeg: Pre-training and Fine-tuning Volumetric Pyramid Transformer for Multi-modal Cardiac Image Segmentation

Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation