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

• Published in: BioMed research international Vol. 2021; pp. 9956983 - 11
• Main Authors: Wei, Xiaoqin, Chen, Xiaowen, Lai, Ce, Zhu, Yuanzhong, Yang, Hanfeng, Du, Yong
• Format: Journal Article
• Language: English
• Published: United States Hindawi 16.12.2021; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Analysis; Anomalies; Artificial neural networks; Aspect ratio; Automation; Boolean algebra; Care and treatment; Classification; Clinical decision making; Clinical medicine; Cluster Analysis; Clustering; Computed tomography; CT imaging; Deep learning; Diagnosis; Generative adversarial networks; Humans; Image classification; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Liver; Liver - diagnostic imaging; Liver cancer; Liver diseases; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Methods; Neural networks; Neural Networks, Computer; Risk factors; Surgery; Tomography; Tomography, X-Ray Computed - methods; Vector quantization; China
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2021/9956983

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.