Deep Learning-based Segmentation of Kidneys from MR Images

Abstract

Abstract Chronic kidney disease (CKD) is a progressive condition affecting millions worldwide, and accurate assessment of kidney structure is essential for early diagnosis and monitoring disease progression. Magnetic Resonance Imaging (MRI) has emerged as a powerful, non-invasive technique for visualizing subtle structural and functional changes of the kidneys, providing insights into disease progression and severity. However, manual segmentation of MRI data is both time-consuming and prone to interand intra-observer variability, highlighting the need for automated methods. This thesis presents a deep learning-based approach for automated segmentation of the renal parenchyma, cortex, and medulla using multi-channel and multi-modal MRI data. A 2D ResUNet architecture was implemented with the Medical Open Network for AI (MONAI) framework and trained on a dataset of 37 MRI scans from CKD patients. Two approaches were evaluated: a multi-channel model utilizing T1- weighted Modified Look-Locker Inversion Recovery (T1-MOLLI) images at multiple inversion times, and a multi-modal model incorporating diffusion-weighted imaging (DWI) and T2*-weighted image data. While the multi-channel T1-MOLLI model demonstrated strong agreement with manual annotations, achieving Dice scores of 0.9089 for parenchyma and 0.8552 for cortex, the multi-modal approach underperformed due to spatial misalignment between input images and reference labels. The proposed segmentation pipeline also enabled reliable quantification of renal parenchyma and cortex volumes, and showed potential for quantifying tissue-specific parametric values relevant to CKD monitoring. However, the reliability of these measurements were highly dependent of the models segmentation performance. Overall, the findings highlight the potential of using deep learning models’ with multichannel MRI input for improving kidney segmentation, serving as a tool to support clinical image analysis workflows and reduce manual effort.