用视频序列思路处理病理切片，提升分割精度。
PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2
- 将切片当作视频帧，利用SAM2记忆机制建模跨切片关系。
- 在KPI Challenge 2024数据集上实现更优的肾小球分割效果。
- 适合需要跨层上下文信息的病理图像分析任务。
当前病理图像分割方法通常独立处理二维切片，忽略跨切片的重要信息。本文提出PathSeqSAM，将二维病理切片视为序列视频帧，借助SAM2的记忆机制进行建模。方法引入距离感知注意力机制，考虑切片间不等距的物理差异，并采用LoRA实现领域适应。在KPI Challenge 2024肾小球分割数据集上的实验表明，该方法在复杂病例中显著提升分割质量，尤其依赖跨切片上下文的场景。代码已公开于https://github.com/JackyyyWang/PathSeqSAM。
原文摘要 · Abstract (English)
Current methods for pathology image segmentation typically treat 2D slices independently, ignoring valuable cross-slice information. We present PathSeqSAM, a novel approach that treats 2D pathology slices as sequential video frames using SAM2's memory mechanisms. Our method introduces a distance-aware attention mechanism that accounts for variable physical distances between slices and employs LoRA for domain adaptation. Evaluated on the KPI Challenge 2024 dataset for glomeruli segmentation, PathSeqSAM demonstrates improved segmentation quality, particularly in challenging cases that benefit from cross-slice context. We have publicly released our code at https://github.com/JackyyyWang/PathSeqSAM.
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