Patient and fraction specific model adaptation for cone beam computed tomography (CBCT) multi-organ segmentation

Cone-beam computed tomography (CBCT) has gained considerable traction in medical imaging due to its ability to provide on-board three-dimensional (3D) volumetric imaging with relatively low radiation dose, aiding in tracking anatomical changes. However, artifacts such as scatter, noise, and respiratory motion, especially in thoracic imaging, complicate accurate segmentation of organs-at-risk (OARs). Additionally, patient-specific anatomical and tumor changes during treatment further challenge precise delineation, potentially affecting radiation therapy outcomes.

This study aims to address the challenges in accurate segmentation of OARs in CBCT imaging, considering the impact of imaging artifacts and patient-specific anatomical and tumor changes, which are critical for improving the efficacy of radiation therapy.

A 3D patient- and fraction-specific scalable and transferable U-Net (PFS-STU-Net) was developed using a large-scale pre-trained STU-Net with LoRA-based parameter-efficient fine-tuning. The framework first adapts a general STU-Net model (G-STU-Net) to the CBCT domain using paired planning CT (pCT) and deformed CBCT (dCBCT) images, then incrementally fine-tunes the model with patient-specific CBCT fractions to capture anatomical evolution throughout treatment. Segmentation accuracy was evaluated using dice similarity coefficient (DSC), 95th-percentile Hausdorff distance (HD95), and mean surface distance (MSD) on 33 lung SBRT patients (5 fractions each) and an external 10-patient cohort. Statistical comparisons were performed using paired Wilcoxon signed-rank tests with Benjamini-Hochberg false discovery rate (FDR) correction for multiple comparisons, with statistical significance set at .

Compared with the pre-trained STU-Net, the proposed PFS-STU-Net achieved statistically significant improvements across all organs ( , FDR-corrected), with large effect sizes (Cliff's $\delta >0.8$ for the esophagus and spinal cord). DSC increased from 0.60 to 0.87 for the esophagus, 0.79 to 0.92 for the heart, 0.83 to 0.98 for the left lung, 0.84 to 0.98 for the right lung, and 0.74 to 0.97 for the spinal cord. Average HD95 and MSD were reduced by more than 50%. External validation demonstrated consistent cross-institutional performance (average DSC = 0.91 ± 0.07).

Integrating large-scale pretraining with LoRA-based patient- and fraction-specific adaptation significantly enhances CBCT multi-organ segmentation accuracy and clinical transferability. The PFS-STU-Net framework demonstrates robust performance across institutions and provides a scalable, efficient solution for real-time CBCT-guided adaptive radiotherapy.