Development and external validation of a multi-task feature fusion network for CTV segmentation in cervical cancer radiotherapy

Abstract

Background and purpose: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues in cervical cancer radiotherapy. Also, although automated CTV segmentation can reduce oncologists' workload, challenges persist due to the microscopic spread of tumor cells undetectable in CT imaging, low-intensity contrast between organs, and inter-observer variability. This study aims to develop and validate a multi-task feature fusion network (MTF-Net) that uses distance-based information to enhance CTV segmentation accuracy.

Materials and methods: We developed a dual-branch, end-to-end MTF-Net designed to address the challenges in cervical cancer CTV segmentation. The MTF-Net architecture consists of a shared encoder and two parallel decoders, one generating a distance location information map (D_img) and the other producing CTV segmentation masks. To enhance segmentation accuracy, we introduced a distance information attention fusion module that integrates features from the Dimg into the CTV segmentation process, thus optimizing target delineation. The datasets for this study were from three centers. Data from two centers were used for model training and internal validation, and that of the third center was used as an independent dataset for external testing. To benchmark performance, we also compared MTF-Net to commercial segmentation software in a clinical setting.

Results: MTF-Net achieved an average dice score of 84.67% on internal and 77.51% on external testing datasets. Compared with commercial software, MTF-Net demonstrated superior performance across several metrics, including Dice score, positive predictive value, and 95% Hausdorff distance, with the exception of sensitivity.

Conclusions: This study indicates that MTF-Net outperforms existing state-of-the-art segmentation methods and commercial software, demonstrating its potential effectiveness for clinical applications in cervical cancer radiotherapy planning.

Keywords: CT images; Cervical cancer radiotherapy; Clinical target volume segmentation; Deep learning; Multi-task learning.