Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant regions in scanned histology slides. However, the techniques necessitate task-specific large datasets of annotated pixels, which is tedious, time-consuming, expensive, and infeasible to acquire for many histology tasks. Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire. In this paper, we propose SegGini, a weakly supervised segmentation method using graphs, that can utilize weak multiplex annotations, i.e. inexact and incomplete annotations, to segment arbitrary and large images, scaling from tissue microarray (TMA) to whole slide image (WSI). Formally, SegGini constructs a tissue-graph representation for an input histology image, where the graph nodes depict tissue regions. Then, it performs weakly-supervised segmentation via node classification by using inexact image-level labels, incomplete scribbles, or both. We evaluated SegGini on two public prostate cancer datasets containing TMAs and WSIs. Our method achieved state-of-the-art segmentation performance on both datasets for various annotation settings while being comparable to a pathologist baseline.

中文翻译：

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使用组织图从不精确和不完整的标签中学习全幻灯片分割

必须将组织学图像分割为诊断相关区域，以支持病理学家及时，可靠地做出决定。为此，已经提出了计算机辅助技术来描绘扫描的组织学幻灯片中的相关区域。然而，这些技术需要注释像素的特定于任务的大型数据集，这对于许多组织学任务而言既乏味，耗时，昂贵且难以获取。因此，提出了弱监督语义分割技术，以利用便宜监督和便宜获取的弱监督。在本文中，我们提出了一种使用图形的弱监督分割方法SegGini，该方法可以利用弱多路复用注解（即不精确和不完整的注解）对任意图像和大图像进行分割，从组织微阵列（TMA）缩放到整个幻灯片图像（WSI） 。形式上，SegGini会为输入的组织学图像构造组织图表示，其中图节点描述组织区域。然后，它通过使用不精确的图像级标签，不完整的涂鸦或同时通过两者进行节点分类，执行弱监督分割。我们在两个包含TMA和WSI的公共前列腺癌数据集上评估了SegGini。我们的方法在各种注释设置的两个数据集上均实现了最新的分割性能，同时可与病理学家的基线相媲美。我们在两个包含TMA和WSI的公共前列腺癌数据集上评估了SegGini。我们的方法在各种注释设置的两个数据集上均实现了最新的分割性能，同时可与病理学家的基线相媲美。我们在两个包含TMA和WSI的公共前列腺癌数据集上评估了SegGini。我们的方法在各种注释设置的两个数据集上均实现了最新的分割性能，同时可与病理学家的基线相媲美。