HAR-Net: Automatic delineation of clinical target volumes for cervical cancer guided by hierarchical anatomical refinement.

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Abstract

In radiotherapy for cervical cancer, accurate delineation of the clinical target volume (CTV) is essential for ensuring prescription dose coverage while minimizing radiation exposure to organs at risk (OARs). In clinical practice, however, CTV boundaries are often poorly defined on standard medical images and exhibit substantial morphological variability. Consequently, automated methods based solely on image appearance or a single segmentation framework may fail to consistently capture the fine-grained anatomical structures and spatial coherence of the CTV. Existing methods are therefore prone to boundary displacement, morphological discontinuity, and inadequate generalizability across patients, which may compromise the reproducibility and clinical applicability of radiotherapy planning. This study aims to develop a hierarchical anatomical refinement network (HAR-Net) for automatic CTV delineation in cervical cancer. By explicitly integrating anatomical priors and spatial relationships in addition to image appearance information, HAR-Net was designed to improve segmentation accuracy and consistency. Computed tomography images and manual delineations of the CTV and 5 OARs, the bladder, rectum, bilateral femoral heads, and small bowel, were retrospectively collected from 132 patients with cervical cancer who received radiotherapy at Xiangya Hospital, Central South University, between May 2022 and May 2025. All patients had stage IB2-IVA disease according to the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system. The delineations were completed by experienced radiation oncologists. Patients were randomly divided into a training set (<i>n</i>=92), validation set (<i>n</i>=15), and test set (<i>n</i>=25). HAR-Net used nnU-Net as its backbone and incorporated 2 collaborative modules. First, multi-scale OAR-CTV cross-attention (MOCCA) dynamically integrated the anatomical context of OARs into the CTV segmentation pathway through window-based three-dimensional cross-attention. Second, tri-axial coordinate attention (TCA) encoded global positional information along the depth, height, and width axes and spatially refined the features through adaptive reweighting. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), 95th-percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD). HAR-Net was compared with several mainstream segmentation methods, including MultiResUNet, AMTA-Net, Swin UNETR, ResUNet-a, and nnU-Net. HAR-Net outperformed multiple state-of-the-art segmentation methods. It achieved a mean DSC of 82.420%, an HD95 of 8.390 mm, and an ASSD of 1.920 mm. Compared to the nnU-Net baseline, HAR-Net improved the DSC by 3.260 percentage points and reduced the HD95 by 4.080 mm. Ablation experiments further demonstrated that both the MOCCA and TCA modules contributed significantly to the improvement in model performance. By hierarchically integrating multi-scale anatomical context from OARs with tri-axial spatial priors, HAR-Net effectively improves the accuracy and anatomical consistency of automatic CTV delineation for cervical cancer. This framework may reduce the need for repeated manual corrections during clinical delineation, decrease the risk of unnecessary radiation exposure to surrounding OARs, and enhance the overall quality and safety of radiotherapy planning. HAR-Net therefore represents a promising auxiliary tool for automated radiotherapy planning.

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