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

• URL: http://arxiv.org/abs/2509.22307v1
• Date: Fri, 26 Sep 2025 13:12:43 GMT
• Title: Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
• Authors: Jinpeng Lu, Linghan Cai, Yinda Chen, Guo Tang, Songhan Jiang, Haoyuan Shi, Zhiwei Xiong,
• Abstract summary: We show how to redesign the framework based on the characteristics of high-dimensional 3D images.<n>Our approach, VeloxSeg, begins with a deployable and dual-stream CNN-Transformer architecture.<n>VeloxSeg achieves a 26% Dice improvement, alongside increasing GPU throughput by 11x and CPU by 48x.
• Score: 42.23472421492995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lightweight 3D medical image segmentation remains constrained by a fundamental "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 11x and CPU by 48x. Codes are available at https://github.com/JinPLu/VeloxSeg.

Related papers

• Wonder3D++: Cross-domain Diffusion for High-fidelity 3D Generation from a Single Image [68.55613894952177]
  We introduce textbfWonder3D++, a novel method for efficiently generating high-fidelity textured meshes from single-view images.<n>We propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images.<n> Lastly, we introduce a cascaded 3D mesh extraction algorithm that drives high-quality surfaces from the multi-view 2D representations in only about $3$ minute in a coarse-to-fine manner.
  arXiv  Detail & Related papers  (2025-11-03T17:24:18Z)
• MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation [3.014234061484863]
  Multiscale Lightweight Residual UNETR++ architecture designed to balance segmentation accuracy and computational efficiency.<n>Experiments on four publicly available benchmark datasets demonstrate that MLRU++ achieves state-of-the-art performance.<n>Results suggest that MLRU++ offers a practical and high-performing solution for 3D medical image segmentation tasks.
  arXiv  Detail & Related papers  (2025-07-22T00:30:44Z)
• MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image Segmentation [7.826754189244901]
  Both CNN-based and Transformer-based methods have achieved remarkable success in medical image segmentation tasks.<n>We propose a novel hybrid CNN-Transformer architecture, named MSLAU-Net, which integrates the strengths of both paradigms.<n>The proposed MSLAU-Net incorporates two key ideas. First, it introduces Multi-Scale Linear Attention, designed to efficiently extract multi-scale features from medical images.<n>Second, it adopts a top-down feature aggregation mechanism, which performs multi-level feature aggregation and restores spatial resolution.
  arXiv  Detail & Related papers  (2025-05-24T18:48:29Z)
• SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation [0.13654846342364302]
  We present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features. We benchmark SegFormer3D against the current SOTA models on three widely used datasets.
  arXiv  Detail & Related papers  (2024-04-15T22:12:05Z)
• Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion [0.0]
  We propose a new 2D-based model dubbed Slice SHift UNet which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three planes of a volume. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ axis (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets.
  arXiv  Detail & Related papers  (2023-07-24T14:53:23Z)
• UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation [93.88170217725805]
  We propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features. Our evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-12-08T18:59:57Z)
• HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities [6.543272301133159]
  Event cameras detect changes in per-pixel intensity to generate asynchronous event streams. They offer great potential for accurate semantic map retrieval in real-time autonomous systems. Existing implementations for event segmentation suffer from sub-based performance. We propose hybrid end-to-end learning framework HALSIE to reduce inference cost by up to $20times$ versus art.
  arXiv  Detail & Related papers  (2022-11-19T17:09:50Z)
• Lightweight and Progressively-Scalable Networks for Semantic Segmentation [100.63114424262234]
  Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. In this paper, we thoroughly analyze the design of convolutional blocks and the ways of interactions across multiple scales. We devise Lightweight and Progressively-Scalable Networks (LPS-Net) that novelly expands the network complexity in a greedy manner.
  arXiv  Detail & Related papers  (2022-07-27T16:00:28Z)
• MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet [55.16833099336073]
  We propose to self-distill a Transformer-based UNet for medical image segmentation. It simultaneously learns global semantic information and local spatial-detailed features. Our MISSU achieves the best performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-02T07:38:53Z)
• A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark [45.543140413399506]
  MedFormer is a data-scalable Transformer designed for generalizable 3D medical image segmentation. Our approach incorporates three key elements: a desirable inductive bias, hierarchical modeling with linear-complexity attention, and multi-scale feature fusion.
  arXiv  Detail & Related papers  (2022-02-28T22:59:42Z)
• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation [95.51455777713092]
  Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. We propose a novel framework that efficiently bridges a bf Convolutional neural network and a bf Transformer bf (CoTr) for accurate 3D medical image segmentation.
  arXiv  Detail & Related papers  (2021-03-04T13:34:22Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.