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.

CRUNet: Cascaded UNet with Residual Mapping for Liver Segmentation in CT Images * Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver segmentation in CT scan is important for diagnosis and analysis of liver lesions. However, the accuracy of existing liver segmentation methods is slightly insufficient. In this paper, we propose a liver segmentation architecture named CRUNet, which is composed of cascade UNet combined with residual mapping. We make use of the MDice loss function for training in CRUNet, and the secondlevel of cascade network is deeper than the firstlevel to extract more detailed image features. Morphological algorithms are utilized as an intermediateprocessing step to improve the segmentation accuracy. In addition, we evaluate our proposed CRUNet on liver segmentation task under the dataset provided by the 2017 ISBI LiTS Challenge. The experimental result demonstrates that our proposed CRUNet can outperform the stateoftheart methods in term of the performance measures, such as Dice score, VOE, and so on.