School of Biomedical Engineering at Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Comput Med Imaging Graph. 2021 Oct;93:101974. doi: 10.1016/j.compmedimag.2021.101974. Epub 2021 Aug 21.
While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.
虽然深度学习模型在医学图像分割任务中表现出色，但由于需要仔细描绘组织结构，因此训练深度学习模型的组织学注释通常具有挑战性。在这项研究中，我们提出了一种无监督方法，称为组织簇级图割（TisCut），用于将组织学图像分割为有意义的区域（例如肿瘤或非肿瘤区域），旨在协助下游监督模型的组织学注释。TisCut 由三个模块组成。首先，根据组织对象的空间接近度和形态特征对其进行聚类。然后基于组织对象聚类构建 Voronoi 图。在最后一个模块中，从 Voronoi 图计算出的形态特征被集成到一个区域邻接图中。然后通过图割算法将图像划分为有意义的区域。TisCut 已经在用于检测坏死和黑色素瘤的三个组织学图像集中进行了评估。实验表明，TisCut 可以提供与 U-Net 模型相当的性能，在分割脑和皮肤组织学图像时，分别可以达到约 70%和 85%的 Jaccard 指数系数。此外，它还显示了在训练掩模难以收集时用于生成组织学注释的潜力。
