Retrospective Comparison of Geometrical Accuracy among Atlas-based Auto-segmentation, Deep Learning Auto-segmentation, and Deformable Image Registration in the Treatment Replanning for Adaptive Radiotherapy of Head-and-Neck Cancer

Abstract

Aims: This study aimed to evaluate the geometrical accuracy of atlas-based auto-segmentation (ABAS), deformable image registration (DIR), and deep learning auto-segmentation (DLAS) in adaptive radiotherapy (ART) for head-and-neck cancer (HNC). Subjects and Methods: Seventeen patients who underwent replanning for ART were retrospectively studied, and delineated contours on their replanning computed tomography (CT2) images were delineated. For DIR, the planning CT image (CT1) of the evaluated patients was utilized. In contrast, ABAS was performed using an atlas dataset comprising 30 patients who were not part of the evaluated group. DLAS was trained with 143 patients from different patients from the evaluated patients. The ABAS model was improved, and a modified ABAS (mABAS) was created by adding the evaluated patients’ own CT1 to the atlas datasets of ABAS (number of patients of the atlas dataset, 31). The geometrical accuracy of DIR, DLAS, ABAS, and mABAS was evaluated. Results: The Dice similarity coefficient in DIR was the highest, at >0.8 at all organs at risk. The mABAS was delineated slightly more accurately than the standard ABAS. There was no significant difference between ABAS and DLAS in delineation accuracy. DIR had the lowest Hausdorff distance (HD) value (within 10 mm). The HD values in ABAS, mABAS, and DLAS were within 16 mm. Conclusions: DIR delineation is the most geometrically accurate ART for HNC.

Keywords

• adaptive radiotherapy
• atlas-based auto-segmentation
• automatic segmentation
• deep learning auto-segmentation
• deformable image registration
• head and neck