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Abstract #0425

Mask R-CNN for Segmentation of Kidneys in Magnetic Resonance Imaging

Manu Goyal1, Junyu Guo1, Lauren Hinojosa1, Keith Hulsey1, and Ivan Pedrosa1
1Radiology, UT Southwestern Medical Center, Dallas, TX, United States

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 a deep learning method called Mask R-CNN for the segmentation of kidneys in 2D coronal T2W FSE images of 94 MRI exams. With 5-fold cross-validation data, the Mask R-CNN is trained and validated on 66 and 9 MRI exams and then evaluated on the remaining 19 exams. Our proposed method achieved an average dice score of 0.839 and an average IoU of 0.763.

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