Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation. 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 DCEMR series as input image; II) the separate phases of the DCEMR as input images; and III) the separate phases of the DCEMR as channels of one input image. The three input configurations are fed into a dilated fully convolutional network and into a small Unet. The CNNs were trained using 19 annotated DCEMR series and tested on another 19 annotated DCEMR 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 DCEMR 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.

Liver Segmentation in CT Images Using a NonLocal Fully Convolutional Neural Network Liver segmentation is a critical step in diagnosing various kinds of hepatic diseases. Based on the segmentation results, physicians can make further assessments more accurately. Although deep learning methods have achieved excellent performance in liver segmentation tasks, the traditional convolution encoderdecoder architecture may easily loss the spatial information due to the stacked convolution and pooling layers. In this paper, we present a nonlocal spatial feature based neural network (referred as NLNet) to learn more spatial features of liver for more accurate segmentation. The NLNet consists of an encoder block, a nonlocal spatial feature learning block and a decoder block. We utilized the pretrained ResNet model with transfer learning as the encoder. The nonlocal block can learn long range dependencies of the liver pixel position by computing the response at a position as a weighted sum of the responses at all positions, which can help the network learn more robust features. We applied the proposed model to ISBI 2019 CHAOs liver Segmentation Challenge task and evaluated it on the testing set. Experimental results show that the proposed NLNet achieved an average dice of 0.972, RAVD of 1.593, ASSD of 1.926 and MSSD of 110.658 on the segmentation results.