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Abstract #3626

Transfer learning of renal cortex segmentation from CT to MRI: facilitated with automatic labeling

Chang Ni1, Xin Mu1, Yanbin Li2, Haikun Qi3,4, and Jeff L. Zhang1
1Vascular and Physiologic Imaging Research (VPIR) Lab, School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 2Central Research Institute,UIH Group, Shanghai, China, 3School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 4Shanghai Clinical Research and Trial Center, Shanghai, China

Synopsis

Keywords: Kidney, SegmentationSegmentation of renal cortex in MR images is important but challenging. In this study, we proposed to pre-train a ResUNet model with CT images and to use an automatic method for labeling renal cortex for the training data. Such method with transfer learning and automatic labeling performed well in segmenting renal cortex in MR images, with a DICE similarity of 0.85 and volume error of 14%±5%. The proposed method would make labeling of renal cortex for training dataset much more efficiently, and we further confirm the power of transfer learning technique in segmenting renal MR images.

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