Enhancing the UNet3+ architecture for deep learning segmentation of kidneys and cysts in autosomal dominant polycystic kidney disease (ADPKD)

Abstract

Autosomal Dominant Polycystic Kidney Disease (ADPKD) presents a significant clinical challenge, demanding precise and efficient diagnostic tools. In this context, this research addresses the imperative need for accurate diagnosis of ADPKD through the refinement of deep learning models, specifically UNet++ and UNet3+, for precise segmentation of renal structures and cysts in T2W MRI images. By incorporating residual staging, switch normalization, and concatenated skip connections (CSC), our proposed models, rUNet++ and rUNet3+, aim to enhance feature fusion, extraction, and segmentation accuracy. Utilizing a dataset of 760 MRI images from 95 patients, we trained, validated, and tested the models, assigning images exclusively to each set to eliminate bias. The ground truth was established through a semi-automated technique. Evaluation metrics, including Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), demonstrated improved performance for rUNet++ and rUNet3+, with the latter exhibiting the highest test minimum DSCs for kidneys and the former for the cysts. The proposed models achieved average DSC scores of 0.95±0.02 and 0.94±0.03 respectively for kidneys and 0.88±0.04 and 0.86±0.05 for cysts. The clinical significance of these findings lies in the enhanced precision of total kidney volume quantification, a vital biomarker for ADPKD diagnosis. This automated approach not only streamlines the diagnostic process but also reduces manual involvement, addressing a crucial aspect in the clinical workflow. By presenting a modified deep learning architecture, this research contributes to advancing the technological landscape in medical imaging, offering a promising avenue for improving the clinical management of ADPKD through accurate and efficient segmentation.