  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.
