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 atthis https URL.
View on arXiv@article{zhu2025_2504.10526,
title={ PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2 },
author={ Mingyang Zhu and Yinting Liu and Mingyu Li and Jiacheng Wang },
journal={arXiv preprint arXiv:2504.10526},
year={ 2025 }
}