Which Way Round? A Study on the Performance of Stain-Translation for Segmenting Arbitrarily Dyed Histological Images

Image-to-image translation based on convolutional neural networks recently gained popularity. Especially approaches relying on generative adversarial networks facilitating unpaired training open new opportunities for image analysis. Making use of an unpaired image-to-image translation approach, we propose a methodology to perform stain-independent segmentation of histological whole slide images requiring annotated training data for one single stain only. In this experimental study, we propose and investigate two different pipelines for performing stain-independent segmentation, which are evaluated with three different stain combinations showing different degrees of difficulty. Whereas one pipeline directly translates the images to be evaluated and uses a segmentation model trained on original data, the other “way round” translates the training data in order to finally segment the original images. The results exhibit good performance especially for the first approach and provide evidence that the direction of translation plays a crucial role considering the final segmentation accuracy.