一种用于肾小球全切片图像分割的通用流程
A General Pipeline for Glomerulus Whole-Slide Image Segmentation
AI总结：
针对全切片图像肾小球分割的需求，提出一种通用流程，通过重叠补丁拼接增强检测覆盖率，经超30K标注的两个数据集评估，模型性能优于现有方法，为该任务建立新基准。
摘要：
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。本研究提出一种通用且实用的肾小球分割流程，可有效增强补丁级和WSI级分割任务。该方法利用重叠补丁的拼接技术，提高检测覆盖率，尤其当肾小球位于补丁图像边界附近时。此外，我们使用两种包含超30K个肾小球标注的大型多样化数据集，对不同分割模型进行全面评估。实验结果表明，采用该流程的模型优于之前的最先进方法，在两个数据集上均取得更优结果，为WSI肾小球分割建立了新基准。代码和预训练模型可在https://github.com/huuquan1994/wsi_glomerulus_seg获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
