探索图像块扫描顺序如何影响Mamba模型在脑部MRI分割中的表现
Flatten Wisely: How Patch Order Shapes Mamba-Powered Vision for MRI Segmentation
- 提出无参数模块MS2D，可灵活测试多种扫描路径
- 实验证明扫描顺序差异可达27个Dice点，显著影响分割效果
- 水平/垂直连续扫描优于对角线等碎片化路径，适合医疗影像
视觉Mamba模型以线性计算成本实现接近Transformer的性能，但其将2D图像序列化为1D序列时，存在一个关键却常被忽视的设计选择：图像块的扫描顺序。在具有强烈解剖先验的医学影像（如脑部MRI）中，该选择至关重要。本文首次系统研究扫描顺序对MRI分割的影响。提出无参数模块Multi-Scan 2D（MS2D），可在不增加计算成本的前提下探索多种扫描路径。我们在三个公开数据集（BraTS 2020、ISLES 2022、LGG）上进行大规模基准测试，涵盖超过7万张切片。分析表明，扫描顺序是统计显著因素（Friedman检验：χ²₂₀=43.9，p=0.0016），性能差异最高达27个Dice点。空间连续路径（如水平和垂直扫描）始终优于不连贯的对角线扫描。结论：扫描顺序是强大且零成本的超参数，本文提供经证据支持的最佳路径清单，以最大化Mamba模型在医学影像中的表现。
原文摘要 · Abstract (English)
Vision Mamba models promise transformer-level performance at linear computational cost, but their reliance on serializing 2D images into 1D sequences introduces a critical, yet overlooked, design choice: the patch scan order. In medical imaging, where modalities like brain MRI contain strong anatomical priors, this choice is non-trivial. This paper presents the first systematic study of how scan order impacts MRI segmentation. We introduce Multi-Scan 2D (MS2D), a parameter-free module for Mamba-based architectures that facilitates exploring diverse scan paths without additional computational cost. We conduct a large-scale benchmark of 21 scan strategies on three public datasets (BraTS 2020, ISLES 2022, LGG), covering over 70,000 slices. Our analysis shows conclusively that scan order is a statistically significant factor (Friedman test: $χ^{2}_{20}=43.9, p=0.0016$), with performance varying by as much as 27 Dice points. Spatially contiguous paths -- simple horizontal and vertical rasters -- consistently outperform disjointed diagonal scans. We conclude that scan order is a powerful, cost-free hyperparameter, and provide an evidence-based shortlist of optimal paths to maximize the performance of Mamba models in medical imaging.
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