Segmenting renal whole slide images virtually without training data

Digital pathology is a field of increasing interest and requires automated systems for processing huge amounts of digital data. The development of supervised-learning based automated systems is aggravated by the fact that image properties can change from slide to slide. In this work, the focus is on the segmentation of the glomeruli constituting the most important regions-of-interest in renal histopathology. We propose and investigate a two-stage pipeline consisting of a weakly supervised patch-based detection and a precise segmentation. The proposed methods do not need any previously obtained training data. For adapting and optimizing this model, a kernel two-sample test is applied. For the segmentation stage, unsupervised segmentation methods including level-set and polygon-fitting approaches are adapted, combined and evaluated. Overall, with the best performing polygon-fitting segmentation method, 51% of glomeruli were segmented with sufficient accuracy (DSC > 0.8). 42% of the detections were false positives. Due to the difficult application scenario in combination with the small required training corpus, the obtained performance is assessed as good. Strategies for increasing the segmentation performance even further are discussed in detail.