Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

ABSTRACT: To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry. We trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy w

SUBMITTER: Wang K 

PROVIDER: S-EPMC7314107 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature