Automated Kidney Segmentation by Mask R-CNN in T2-weighted Magnetic Resonance Imaging
This work addresses the need for automated kidney segmentation in MRI to enable radiomics and machine learning analysis of renal disease, but it is incremental as it adapts an existing method with minor improvements.
The authors tackled the problem of automated kidney segmentation in T2-weighted MRI, which is scarce despite deep learning advances, by applying Mask R-CNN with morphological post-processing, achieving a dice score of 0.904 and IoU of 0.822 on a dataset of 100 MRI exams.
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.