Cyst segmentation on kidney tubules by means of U-Net deep-learning models

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most widespread genetic disorders affecting the kidney. Nevertheless, there is still no cure for ADPKD. Domain experts test the effectiveness of different treatments by investigating how they can reduce the number and dimension of cysts on kidney tissues. Image processing of the microscope acquisitions is then an expensive but necessary operation currently performed by operators to determine and compare cyst size and quantity. In this work, we propose a deep learning algorithm for fast and accurate cysts detection in sequential 2-D images. Experiments on 507 RGB immunofluorescence images of 8 kidney tubules show that the proposed U-Net-based deep-learning solution can automatically segment images with increasing performance at larger cyst dimensions (Pr > 0.8, Re > 0.75 for cysts larger than 32 µm 2 ). Such a reliable method performing an accurate cyst segmentation can be a valid support for researchers in optimising the effort to find new effective treatments for ADPKD.