Multi-Organ Gland Segmentation Using Deep Learning
Multi-Organ Gland Segmentation Using Deep Learning
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DOI：
10.3389/fmed.2019.00173
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发表时间：
2019-08-05
影响因子：
3.9
通讯作者：
Gabrani, Maria
中科院分区：
文献类型：
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作者：
Binder, Thomas;Tantaoui, El Mehdi;Gabrani, Maria
组织病理学标本的临床形态分析是肿瘤诊断的有效方法。计算病理学方法可以用来自动化这种分析，提供改进的客观性和可伸缩性。更具体地说，计算机技术可以用于分割腺体，这是癌症诊断中的一个基本因素。考虑到腺体形态在不同组织和病理亚型之间的很大差异，腺体的自动描绘是一项具有挑战性的任务。一种基于深度学习的腺体分割方法可以解决上述问题，但它需要从多个组织切片中获得大量准确的腺体标注。如此庞大的数据集需要由经验丰富的病理医生手动生成，费力、耗时、昂贵，而且受制于注释者的主观性。到目前为止，深度学习技术已经在一些特定于器官的腺体分割任务中取得了有希望的结果，然而，对特定于器官的腺体注释的需求阻碍了这些技术向其他器官的扩展。这项工作研究了跨域(器官类型)近似的想法，旨在减少对器官特定注释的需求。与实质组织不同，位于腺体之间的组织的基质成分在几个器官中更一致。据推测，一种能够精确分割间质的自动方法将为跨器官腺体分割铺平道路。对H&E应变的结肠腺癌样本训练了两个密集U-网，重点是腺体和间质的分割。训练好的网络在两个独立的数据集上进行了评估，它们是H&E染色的结肠腺癌数据集和H&E染色的乳腺浸润性癌数据集。以间质分割为目标的训练网络执行类似于以结肠数据集上的腺体分割为目标的网络。然而，前一种方法在乳腺数据集上的性能明显好于后一种方法，显示了间质分割方法更高的泛化能力。网络的评估使用Dice系数和计算的地面真相腺体掩模和预测腺体掩模之间的Hausdorff距离。实验结果验证了该方法对多器官腺体分割的有效性。
Clinical morphological analysis of histopathology samples is an effective method in cancer diagnosis. Computational pathology methods can be employed to automate this analysis, providing improved objectivity and scalability. More specifically, computational techniques can be used in segmenting glands, which is an essential factor in cancer diagnosis. Automatic delineation of glands is a challenging task considering a large variability in glandular morphology across tissues and pathological subtypes. A deep learning based gland segmentation method can be developed to address the above task, but it requires a large number of accurate gland annotations from several tissue slides. Such a large dataset need to be generated manually by experienced pathologists, which is laborious, time-consuming, expensive, and suffers from the subjectivity of the annotator. So far, deep learning techniques have produced promising results on a few organ-specific gland segmentation tasks, however, the demand for organ-specific gland annotations hinder the extensibility of these techniques to other organs. This work investigates the idea of cross-domain (-organ type) approximation that aims at reducing the need for organ-specific annotations. Unlike parenchyma, the stromal component of tissues, that lies between the glands, is more consistent across several organs. It is hypothesized that an automatic method, that can precisely segment the stroma, would pave the way for a cross-organ gland segmentation. Two proposed Dense-U-Nets are trained on H&E strained colon adenocarcinoma samples focusing on the gland and stroma segmentation. The trained networks are evaluated on two independent datasets, they are, a H&E stained colon adenocarcinoma dataset and a H&E stained breast invasive cancer dataset. The trained network targeting the stroma segmentation performs similar to the network targeting the gland segmentation on the colon dataset. Whereas, the former approach performs significantly better compared to the latter approach on the breast dataset, showcasing the higher generalization capacity of the stroma segmentation approach. The networks are evaluated using Dice coefficient and Hausdorff distance computed between the ground truth gland masks and the predicted gland masks. The conducted experiments validate the efficacy of the proposed stoma segmentation approach toward multi-organ gland segmentation.