부산대학교 도서관

Magnetic Resonance urography (MRU) can be used to evaluate abnormalities of the urinary tract in children, with the advantage of being a non-invasive technique and allowing both morphologic and functional assessments. Today, MRU analysis is usually performed using semi-automatic software that typically requires manual segmentation of kidney and pelvis and other time-consuming interactions. In this work, we propose a deep learning approach to automatize the functional MRU analysis. Our pipeline first employs an Attention U-Net for kidney and pelvis segmentation on morphological magnetic resonance and then an image registration process to align the segmentations on the functional MR. The automatic segmentation of morphological MR has been tested on 107 patients using cross validation, achieving a dice score of $\mathbf{0.87}\pm \mathbf{0.15}$ and $\mathbf{0.91}\pm \mathbf{0.11}$ for left and right kidney, and a dice score of $\mathbf{0.75}\pm \mathbf{0.24}$ and $\mathbf{0.71}\pm \mathbf{0.25}$ for the left and right pelvis respectively. These segmentations are used to extract morphological and functional parameters to assess the urinary-tract function in children undergoing analysis. The proposed approach has been integrated into a commercial web viewer (DicomVision 0.18.3) so that it can be used easily by clinical staff. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of children MRU, thus laying the pave for its wider exploitation in clinical routines.