用千分之一参数量实现顶尖脑卒中病灶分割，无需传统结构
State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
- 创新多尺度膨胀模式，绕过下采样与跳跃连接
- 在256³全脑MRI上达到顶尖分割精度，参数量仅为1/1000
- 适合网页端等资源受限场景，推动医疗影像工具普及
高效且准确的全脑病灶分割在医学图像分析中仍具挑战。本文重新审视参数高效的分割模型MeshNet，提出一种新型多尺度膨胀模式，结合编码器-解码器结构，可在不使用传统下采样、上采样或跳跃连接的情况下捕捉全局上下文与细粒度细节。与以往处理子体积或切片的方法不同，本方法直接作用于全脑256³ MRI体积。在Aphasia Recovery Cohort（ARC）数据集上的评估表明，MeshNet在参数量仅为1/1000的前提下，达到或超过MedNeXt与U-MAMBA等前沿架构的DICE分数。结果验证了MeshNet在效率与性能间的优异平衡，特别适用于网页应用等资源受限环境，为先进医学图像分析工具的广泛应用开辟新路径。
原文摘要 · Abstract (English)
Efficient and accurate whole-brain lesion segmentation remains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-efficient segmentation model, and introduce a novel multi-scale dilation pattern with an encoder-decoder structure. This innovation enables capturing broad contextual information and fine-grained details without traditional downsampling, upsampling, or skip-connections. Unlike previous approaches processing subvolumes or slices, we operate directly on whole-brain $256^3$ MRI volumes. Evaluations on the Aphasia Recovery Cohort (ARC) dataset demonstrate that MeshNet achieves superior or comparable DICE scores to state-of-the-art architectures such as MedNeXt and U-MAMBA at 1/1000th of parameters. Our results validate MeshNet's strong balance of efficiency and performance, making it particularly suitable for resource-limited environments such as web-based applications and opening new possibilities for the widespread deployment of advanced medical image analysis tools.
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