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Most MRI liver segmentation methods use a structural 3D scan as input, suchas a T1 or T2 weighted scan. Segmentation performance may be improved byutilizing both structural and functional information, as contained in dynamiccontrast enhanced (DCE) MR series. Dynamic information can be incorporated in asegmentation method based on convolutional neural networks in a number of ways.In this study, the optimal input configuration of DCE MR images forconvolutional neural networks (CNNs) is studied. The performance of threedifferent input configurations for CNNs is studied for a liver segmentationtask. The three configurations are I) one phase image of the DCE-MR series asinput 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 threeinput configurations are fed into a dilated fully convolutional network andinto a small U-net. The CNNs were trained using 19 annotated DCE-MR series andtested on another 19 annotated DCE-MR series. The performance of the threeinput configurations for both networks is evaluated against manual annotations.The results show that both neural networks perform better when the separatephases of the DCE-MR series are used as channels of an input image incomparison to one phase as input image or the separate phases as input images.No significant difference between the performances of the two networkarchitectures was found for the separate phases as channels of an input image.

Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation