面向肾脏病理学全切片成像的多尺度多中心肾脏微血管结构分割
Multi-scale Multi-site Renal Microvascular Structures Segmentation for Whole Slide Imaging in Renal Pathology
AI总结：
本文提出Omni-Seg单一动态网络，利用HuBMAP和NEPTUNE多中心多尺度部分标注数据，实现肾脏全切片图像中微血管结构的高精度分割，并在DSC和IoU指标上优于现有方法。
摘要：
从人类肾脏全切片图像（WSI）中分割微血管结构（如小动脉、小静脉和毛细血管）已成为肾脏病理学的一个焦点。目前的手动分割技术耗时且不适用于大规模数字病理图像。虽然基于深度学习的方法为自动分割提供了解决方案，但大多数方法存在一个局限：它们仅针对单中心、单尺度数据进行设计和训练。在本文中，我们提出了Omni-Seg，一种新颖的单一动态网络方法，该方法充分利用多中心、多尺度训练数据。我们方法的独特之处在于，我们利用部分标注图像（每张训练图像仅标注一种组织类型）来分割微血管结构。我们使用来自HuBMAP和NEPTUNE两个数据集、不同放大倍数（40倍、20倍、10倍和5倍）的图像训练单一深度网络。实验结果表明，Omni-Seg在Dice相似系数（DSC）和交并比（IoU）方面均表现更优。我们提出的方法为肾脏病理学家提供了一种强大的计算工具，用于肾脏微血管结构的定量分析。
英文摘要：
Segmentation of microvascular structures, such as arterioles, venules, and capillaries, from human kidney whole slide images (WSI) has become a focal point in renal pathology. Current manual segmentation techniques are time-consuming and not feasible for large-scale digital pathology images. While deep learning-based methods offer a solution for automatic segmentation, most suffer from a limitation: they are designed for and restricted to training on single-site, single-scale data. In this paper, we present Omni-Seg, a novel single dynamic network method that capitalizes on multi-site, multi-scale training data. Unique to our approach, we utilize partially labeled images, where only one tissue type is labeled per training image, to segment microvascular structures. We train a singular deep network using images from two datasets, HuBMAP and NEPTUNE, across different magnifications (40x, 20x, 10x, and 5x). Experimental results indicate that Omni-Seg outperforms in terms of both the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). Our proposed method provides renal pathologists with a powerful computational tool for the quantitative analysis of renal microvascular structures.
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。
