Four-dimensional fully convolutional residual network-based liver segmentation in Gd-EOB-DTPA-enhanced MRI

Tomomi Takenaga1,2, Shouhei Hanaoka3, Yukihiro Nomura4

  • 1Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. takenaga-tky@umin.ac.jp.

Summary

A new four-dimensional (4D) fully convolutional residual network (FC-ResNet) accurately segments livers in gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI scans. This method improves preprocessing for computer-assisted detection (CAD) software.

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