Despite the recent advances of deep learning algorithms in medical imaging, automatic segmentation algorithms for kidneys in Magnetic Resonance Imaging (MRI) examinations are lacking. Automated segmentation of kidneys in MRI can enable several clinical applications and use of radiomics and machine learning analysis of renal disease. In this work, we propose the application of a Mask R-CNN for the automatic segmentation of the kidneys in coronal T2- weighted single-shot fast spin echo MRI. 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 was trained and validated on 70 and 10 MRI exams, respectively, and then evaluated on the remaining 20 exams in each fold. Our proposed method achieved a dice score of 0.905 and Intersection over Union of 0.828.
Respiratory motion is a major contributor to bias in quantitative analysis of magnetic resonance imaging (MRI) acquisitions. Deformable registration of three-dimensional (3D) dynamic contrast-enhanced (DCE) MRI data improves estimation of kidney kinetic parameters. In this study, we proposed a deep learning approach with two steps: a convolutional neural network (CNN) based affine registration network, followed by a U-Net trained for deformable registration between two MR images. The proposed registration method was applied successively across consecutive dynamic phases of the 3D DCE-MRI dataset to reduce motion effects in the different kidney compartments (i.e., cortex, medulla). Successful reduction in the motion effects caused by patient respiration during image acquisition allows for improved kinetic analysis of the kidney. Original and registered images were analyzed and compared using dynamic intensity curves of the kidney compartments, target registration error of anatomical markers, image subtraction, and simple visual assessment. The proposed deep learning-based approach to correct motion effects in abdominal 3D DCE-MRI data can be applied to various kidney MR imaging applications.
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