Whole-slide images (WSI) glomerulus segmentation is essential for accurately diagnosing kidney diseases. In this work, we propose a 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.

翻译：全切片图像（WSI）肾小球分割对于准确诊断肾脏疾病至关重要。本研究提出了一种实用的肾小球分割流程，能有效增强局部图像块级别和全切片级别的分割任务。我们的方法利用重叠图像块的拼接处理，提高了检测覆盖率，尤其当肾小球位于图像块边界附近时效果显著。此外，我们在两个包含超过3万个肾小球标注的大型多样化数据集上，对多种分割模型进行了全面评估。实验结果表明，采用本流程的模型在两项数据集上均超越了现有最优方法，取得了更优异的分割效果，为WSI肾小球分割建立了新的性能基准。相关代码与预训练模型已发布于 https://github.com/huuquan1994/wsi_glomerulus_seg。