Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.

Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional neural networks (Mask R-CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R-CNN and GANs in order to enhance the pixel-wise classification. Secondly, k-means clustering was used to lock the image aspect ratio, in order to get more essential anchors which can help boost the segmentation performance. Finally, we proposed a GAN Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN, Mask-CNN, and k-means algorithms in terms of the Dice similarity coefficient (DSC) and the MICCAI metrics. The proposed algorithm also achieved superior performance in comparison with ten state-of-the-art algorithms in terms of six Boolean indicators. We hope that our work can be effectively used to optimize the segmentation and classification of liver anomalies.

Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks.

BACKGROUND: Liver segmentation in computed tomography (CT) imaging has been widely investigated as a crucial step for analyzing liver characteristics and diagnosing liver diseases. However, obtaining satisfactory liver segmentation performance is highly challenging because of the poor contrast between the liver and its surrounding organs and tissues, the high levels of CT image noise, and the wide variability in liver shapes among patients. METHODS: To overcome these challenges, we propose a novel method for liver segmentation in CT image sequences. This method uses an enhanced mask region-based convolutional neural network (Mask R-CNN) with graph-cut segmentation. Specifically, the k-nearest neighbor (k-NN) algorithm is employed to cluster the target liver pixels in order to get an appropriate aspect ratio. Then, anchors are adapted to the liver size using the ratio information. Thus, high-accuracy liver localization can be achieved using the anchors and rotation-invariant object recognition. Next, a fully convolutional network (FCN) is used to segment the foreground objects, and local fine-grained liver detection is realized by pixel prediction. Finally, a whole liver mask is obtained by Mask R-CNN proposed in this paper. RESULTS: We proposed a Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN algorithms in term of the dice similarity coefficient (DSC), and the Medical Image Computing and Computer-Assisted Intervention (MICCAI) metrics. CONCLUSIONS: Our experimental results demonstrate that the improved Mask R-CNN architecture has good performance, accuracy, and robustness for liver segmentation in CT image sequences.