IEEE Trans Med Imaging. 2019 Oct;38(10):2293-2302. doi: 10.1109/TMI.2019.2899364. Epub 2019 Feb 14.
A major challenge in the field of segmentation in digital pathology is given by the high effort for manual data annotations in combination with many sources introducing variability in the image domain. This requires methods that are able to cope with variability without requiring to annotate a large amount of samples for each characteristic. In this paper, we develop approaches based on adversarial models for image-to-image translation relying on unpaired training. Specifically, we propose approaches for stain-independent supervised segmentation relying on image-to-image translation for obtaining an intermediate representation. Furthermore, we develop a fully-unsupervised segmentation approach exploiting image-to-image translation to convert from the image to the label domain. Finally, both approaches are combined to obtain optimum performance in unsupervised segmentation independent of the characteristics of the underlying stain. Experiments on patches showing kidney histology proof that stain-translation can be performed highly effectively and can be used for domain adaptation to obtain independence of the underlying stain. It is even capable of facilitating the underlying segmentation task, thereby boosting the accuracy if an appropriate intermediate stain is selected. Combining domain adaptation with unsupervised segmentation finally showed the most significant improvements.
在数字病理学的分割领域中，一个主要的挑战是手动数据注释需要大量的工作，并且许多来源会导致图像域的变异性。这就需要能够应对变异性的方法，而不需要为每个特征注释大量的样本。在本文中，我们开发了基于对抗模型的图像到图像翻译方法，该方法依赖于无配对训练。具体来说，我们提出了基于染色独立性监督分割的方法，该方法依赖于图像到图像的翻译来获得中间表示。此外，我们还开发了一种完全无监督的分割方法，利用图像到图像的翻译从图像转换到标签域。最后，这两种方法结合起来，在不依赖于基础染色特征的情况下，实现了无监督分割的最佳性能。在显示肾脏组织学的补丁上的实验证明，染色转换可以非常有效地进行，并且可以用于域自适应，以获得对基础染色的独立性。如果选择了适当的中间染色，它甚至能够促进基础分割任务，从而提高准确性。将域自适应与无监督分割相结合最终显示出了最显著的改进。
