提出通用流程提升肾小球全切片图像分割精度
A General Pipeline for Glomerulus Whole-Slide Image Segmentation
- 采用重叠切片拼接策略，增强边界区域检测能力
- 在超3万标注数据上验证，显著超越现有最佳方法
- 适合病理图像分析与医学影像算法研究者使用
全切片图像（WSI）中的肾小球分割对准确诊断肾脏疾病至关重要。本文提出一种通用且实用的肾小球分割流程，有效提升局部切片与整体全切片层面的分割性能。该方法通过重叠切片拼接，增强了检测覆盖范围，尤其改善了位于切片边界的肾小球定位。我们在两个大规模、多样化的数据集上进行了全面评估，包含超过3万条肾小球标注。实验结果表明，采用本流程的模型在两个数据集上均优于先前最先进方法，为WSI中的肾小球分割设立了新基准。代码与预训练模型已公开于https://github.com/huuquan1994/wsi_glomerulus_seg。
原文摘要 · Abstract (English)
Whole-slide images (WSI) glomerulus segmentation is essential for accurately diagnosing kidney diseases. In this work, we propose a general and practical pipeline for glomerulus segmentation that effectively enhances both patch-level and WSI-level segmentation tasks. Our approach leverages stitching on overlapping patches, increasing the detection coverage, especially when glomeruli are located near patch image borders. In addition, we conduct comprehensive evaluations from different segmentation models across two large and diverse datasets with over 30K glomerulus annotations. Experimental results demonstrate that models using our pipeline outperform the previous state-of-the-art method, achieving superior results across both datasets and setting a new benchmark for glomerulus segmentation in WSIs. The code and pre-trained models are available at https://github.com/huuquan1994/wsi_glomerulus_seg.
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