Abstract #2295

Combining Convolutional Neural Networks, Multiparametric MRI, and Error Detection to Improve Automated Liver Segmentation

Matthew Gibbons¹, Edgar Castellanos Diaz¹, Suneil K Koliwad², Peter W Hunt², Jean-Marc Schwarz^2,3, Kathleen Mulligan^2,3, Robert H Lustig⁴, Alejandro Gugliucci³, Diana L Alba², Ayca Erkin-Cakmak⁴, and Susan M Noworolski¹

¹Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Department of Medicine, University of California San Francisco, San Francisco, CA, United States, ³College of Osteopathic Medicine, Touro University - California, Vallejo, CA, United States, ⁴Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States

Synopsis

The objective of this study was to generate an automatic liver segmentation method. Two methods were compared. The first, M1, was a Convolutional Neural Network (CNN) trained on proton density fat fraction (PDFF) maps. The second, M2, was the CNN trained on multiparametric MRI (mpMRI) images combined with an error detection protocol. The distributions for Dice similarity coefficient (DSC), volume, and PDFF were improved for M2 versus M1. The DSC mean increased from 0.91 to 0.96. The M2 method was effective in detecting and correcting poor segmentations while significantly reducing processing time as compared to manual segmentation.

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