Interstitial-guided automatic clinical tumor volume segmentation network for cervical cancer brachytherapy.
Authors
Affiliations (7)
Affiliations (7)
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 101400, China.
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
- Liaoning Cancer Hospital and Institute, Shenyang, Liaoning 110042, China.
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong SAR, China.
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates.
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. Electronic address: [email protected].
Abstract
Automatic clinical tumor volume (CTV) delineation is pivotal to improving outcomes for interstitial brachytherapy cervical cancer. However, the prominent differences in gray values due to the interstitial needles bring great challenges on deep learning-based segmentation model. In this study, we proposed a novel interstitial-guided segmentation network termed advance reverse guided network (ARGNet) for cervical tumor segmentation with interstitial brachytherapy. Firstly, the location information of interstitial needles was integrated into the deep learning framework via multi-task by a cross-stitch way to share encoder feature learning. Secondly, a spatial reverse attention mechanism is introduced to mitigate the distraction characteristic of needles on tumor segmentation. Furthermore, an uncertainty area module is embedded between the skip connections and the encoder of the tumor segmentation task, which is to enhance the model's capability in discerning ambiguous boundaries between the tumor and the surrounding tissue. Comprehensive experiments were conducted retrospectively on 191 CT scans under multi-course interstitial brachytherapy. The experiment results demonstrated that the characteristics of interstitial needles play a role in enhancing the segmentation, achieving the state-of-the-art performance, which is anticipated to be beneficial in radiotherapy planning.