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arXiv · 2503.05531

State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters

Alex Fedorov·Yutong Bu·Xiao Hu·Chris Rorden·Sergey Plis
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Abstract

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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Alex Fedorov, Yutong Bu, Xiao Hu, Chris Rorden, Sergey Plis. 2025-03-07. State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters. https://arxiv.org/abs/2503.05531

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