C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical...

Precise delineation of pelvic organs-at-risk (OARs) is crucial for high-dose-rate brachytherapy (HDR-BT) in cervical cancer treatment. While deep learning methods have shown promise in automatic delineation, substantial and complex organ deformations pose significant challenges. This study presents a novel approach to address these issues.

We introduce a coarse-to-refine strategy for annotation, utilizing limited existing data to expedite the process. Combined with deformation-based data augmentation, we incorporate this information into a three-dimensional attention U-Net (C2FAU-Net). The study included 100 cervical cancer patients, with OARs annotated by experienced oncologists. The dataset was divided into 80 patients for training, 10 for validation, and 10 for testing. To assess the delineation performance, we employed the volumetric dice similarity coefficient (DSC), 95^th percentile Hausdorff Distance (HD95), average symmetric surface distance (ASSD), precision, and recall. We compared the time consumed by manual delineation versus artificial intelligence (AI)-assisted delineation. Dosimetric parameters were compared using different contours to evaluate the clinical impact of the automated approach.

Our method achieved an average DSC of 89.7%, HD95 of 3.61 mm, and an ASSD of 1.02 mm in the test cohort. The AI-assisted method significantly reduced the manual delineation time from 17.85 ± 3.84 min to 7.54 ± 4.95 min. No significant difference was observed in ΔD_2cc, ΔD_1cc, ΔD_0.1cc, and ΔD_max for bladder, rectum, and sigmoid when comparing contours generated by C2FAU-Net to those created manually.

We introduce an effective automatic delineation framework for pelvic OARs, enhancing efficiency within the HDR-BT workflow and potentially improving treatment outcomes.