A full-scale attention-augmented CNN-transformer model for segmentation of oropharyngeal mucosa organs-at-risk in radiotherapy.

Abstract

Radiation-induced oropharyngeal mucositis (ROM) is a common and severe side effect of radiotherapy in nasopharyngeal cancer patients, leading to significant clinical complications such as malnutrition, infections, and treatment interruptions. Accurate delineation of the oropharyngeal mucosa (OPM) as an organ-at-risk (OAR) is crucial to minimizing radiation exposure and preventing ROM. This study aims to develop and validate an advanced automatic segmentation model, attention-augmented Swin U-Net transformer (AA-Swin UNETR), for accurate delineation of OPM to improve radiotherapy planning and reduce the incidence of ROM. We proposed a hybrid CNN-transformer model, AA-Swin UNETR, based on the Swin UNETR framework, which integrates hierarchical feature extraction with full-scale attention mechanisms. The model includes a Swin Transformer-based encoder and a CNN-based decoder with residual blocks, connected via a full-scale feature connection scheme. The full-scale attention mechanism enables the model to capture long-range dependencies and multi-level features effectively, enhancing the segmentation accuracy. The model was trained on a dataset of 202 CT scans from Nanfang Hospital, using expert manual delineations as the gold standard. We evaluated the performance of AA-Swin UNETR against state-of-the-art (SOTA) segmentation models, including Swin UNETR, nnUNet, and 3D UX-Net, using geometric and dosimetric evaluation parameters. The geometric metrics include Dice similarity coefficient (DSC), surface DSC (sDSC), volume similarity (VS), Hausdorff distance (HD), precision, and recall. The dosimetric metrics include changes of D_0.1 cc and D_mean between results derived from manually delineated OPM and auto-segmentation models. The AA-Swin UNETR model achieved the highest mean DSC of 87.72 ± 1.98%, significantly outperforming Swin UNETR (83.53 ± 2.59%), nnUNet (85.48%± 2.68), and 3D UX-Net (80.04 ± 3.76%). The model also showed superior mean sDSC (98.44 ± 1.08%), mean VS (97.86 ± 1.43%), mean precision (87.60 ± 3.06%) and mean recall (89.22 ± 2.70%), with a competitive mean HD of 9.03 ± 2.79 mm. For dosimetric evaluation, the proposed model generates smallest mean [Formula: see text] (0.46 ± 4.92 cGy) and mean [Formula: see text] (6.26 ± 24.90 cGY) relative to manual delineation compared with other auto-segmentation results (mean [Formula: see text] of Swin UNETR = -0.56 ± 7.28 cGy, nnUNet = 0.99 ± 4.73 cGy, 3D UX-Net = -0.65 ± 8.05 cGy; mean [Formula: see text] of Swin UNETR = 7.46 ± 43.37, nnUNet = 21.76 ± 37.86 and 3D UX-Net = 44.61 ± 62.33). In this paper, we proposed a transformer and CNN hybrid deep-learning based model AA-Swin UNETR for automatic segmentation of OPM as an OAR structure in radiotherapy planning. Evaluations with geometric and dosimetric parameters demonstrated AA-Swin UNETR can generate delineations close to a manual reference, both in terms of geometry and dose-volume metrics. The proposed model out-performed existing SOTA models in both evaluation metrics and demonstrated is capability of accurately segmenting complex anatomical structures of the OPM, providing a reliable tool for enhancing radiotherapy planning.