Morphometry-based detection of deep learning faults in glomerular segmentation

Abstract

Deep learning-based segmentation has evolved to a powerful strategy for automatically annotating glomeruli in kidney biopsy images. However, since any artificial intelligence can make mistakes, strategies for identifying and correcting faulty annotations are often indispensable. Yet, how can such a validation be achieved without the laborious task of a pathologist manually checking every single image? To address this issue, the current project performed an extensive study on the use of shape analysis to automatically evaluate the glomerular annotations produced by deep-learning segmentation. Examining a large repertoire of shape descriptors on over 168000 glomerular predictions, the study found that morphometry could successfully highlight and distinguish between three different types of segmentation inconsistencies. In addition, using shape descriptors to rank segmentation annotations, it was possible to obtain a distinct enrichment of errors on the leading edge of the ranking, implying that pathologists would only have to inspect and correct the most suspicious fraction of all annotations. Ultimately, the study suggested a panel of three shape descriptors that enabled an efficient enrichment of all errors, respective or irrespective of error type. In summary, the work demonstrates the methodological aspects and benefits of shape analysis for evaluating glomerular segmentation results. We are convinced that, by applying such a strategy for detecting segmentation errors, it will be possible to approach a more time-efficient correction of deep learning-derived glomerular annotations.