Automated Kidney Segmentation by Mask R-CNN in T2-weighted Magnetic Resonance Imaging

TL;DR

This paper presents an automated kidney segmentation method in T2-weighted MRI using Mask R-CNN, enhanced with morphological post-processing, achieving high accuracy in a dataset of 100 exams.

Contribution

The study introduces the application of Mask R-CNN with morphological post-processing for kidney segmentation in MRI, demonstrating high accuracy with a novel combination of techniques.

Findings

Dice score of 0.904 achieved

IoU of 0.822 achieved

Effective in coronal T2-weighted MRI slices

Abstract

Despite the recent advances of deep learning algorithms in medical imaging, the automatic segmentation algorithms for kidneys in MRI exams are still scarce. Automated segmentation of kidneys in Magnetic Resonance Imaging (MRI) exams are important for enabling radiomics and machine learning analysis of renal disease. In this work, we propose to use the popular Mask R-CNN for the automatic segmentation of kidneys in coronal T2-weighted Fast Spin Eco slices of 100 MRI exams. We propose the morphological operations as post-processing to further improve the performance of Mask R-CNN for this task. With 5-fold cross-validation data, the proposed Mask R-CNN is trained and validated on 70 and 10 MRI exams and then evaluated on the remaining 20 exams in each fold. Our proposed method achieved a dice score of 0.904 and IoU of 0.822.