Share Email Print
cover

Proceedings Paper • new

Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
Author(s): Mariëlle J. A. Jansen; Hugo J. Kuijf; Josien P. W. Pluim
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Most MRI liver segmentation methods use a structural 3D scan as input, such as a T1 or T2 weighted scan. Segmentation performance may be improved by utilizing both structural and functional information, as contained in dynamic contrast enhanced (DCE) MR series. Dynamic information can be incorporated in a segmentation method based on convolutional neural networks in a number of ways. In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied. The performance of three different input configurations for CNNs is studied for a liver segmentation task. The three configurations are I) one phase image of the DCE-MR series as input image; II) the separate phases of the DCE-MR as input images; and III) the separate phases of the DCE-MR as channels of one input image. The three input configurations are fed into a dilated fully convolutional network and into a small U-net. The CNNs were trained using 19 annotated DCE-MR series and tested on another 19 annotated DCE-MR series. The performance of the three input configurations for both networks is evaluated against manual annotations. The results show that both neural networks perform better when the separate phases of the DCE-MR series are used as channels of an input image in comparison to one phase as input image or the separate phases as input images. No significant difference between the performances of the two network architectures was found for the separate phases as channels of an input image.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491V (15 March 2019); doi: 10.1117/12.2506770
Show Author Affiliations
Mariëlle J. A. Jansen, Univ. Medical Ctr. Utrecht (Netherlands)
Hugo J. Kuijf, Univ. Medical Ctr. Utrecht (Netherlands)
Josien P. W. Pluim, Univ. Medical Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top