SAM-based Automatic Workflow for Histology Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease

Abstract

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder characterized by the development of numerous cysts in the kidneys, ultimately leading to significant structural alterations and renal failure. Detailed investigations of this disease frequently utilize histological analyses of kidney sections across various stages of ADPKD progression. In this paper, we introduce an automated workflow leveraging the Segment Anything Model (SAM) neural network, complemented by a series of post-processing steps, to autonomously segment cysts in histological images. This approach eliminates the need for manual annotations or preliminary training phases and enables precise quantification of cystic changes over entire kidney sections. Application of this method to sequential histology images across the development timeline of ADPKD in mice demonstrated a notable increase in the proportion of diseased tissue from 8 to 12 weeks and from 12 to 16 weeks, with the cysts appearing progressively lighter. Our workflow not only surpasses the performance of the existing Cystanalyser tool but also offers enhanced flexibility and accuracy in full-image segmentation. The developed workflow is made publicly accessible to facilitate its adoption as an efficient tool for rapid and reliable cyst segmentation in histological studies.