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...
Saved in:
Published in | BioMed research international Vol. 2021; pp. 9956983 - 11 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Hindawi
16.12.2021
John Wiley & Sons, Inc Hindawi Limited |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Academic Editor: Marco Aiello |
ISSN: | 2314-6133 2314-6141 |
DOI: | 10.1155/2021/9956983 |