In Medical physics ; h5-index 59.0
PURPOSE : Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images, which often have severe artifacts and lack soft tissue contrast, making direct segmentation very challenging. Propagating expert-drawn contours from the pre-treatment planning CT (pCT) through traditional or deep learning (DL) based deformable image registration (DIR) can achieve improved results in many situations. Typical DL-based DIR models are population-based, that is, trained with a dataset for a population of patients, and so they may be affected by the generalizability problem.
METHODS : In this paper, we propose a method called test-time optimization (TTO) to refine a pre-trained DL-based DIR population model, first for each individual test patient, and then progressively for each fraction of online ART treatment. Our proposed method is less susceptible to the generalizability problem, and thus can improve overall performance of different DL-based DIR models by improving model accuracy, especially for outliers. Our experiments used data from 239 patients with head and neck squamous cell carcinoma to test the proposed method. Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models. We compared each of the individualized models with the population model in terms of segmentation accuracy.
RESULTS : The average improvement of the Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD95) of segmentation can be up to 0.04 (5%) and 0.98 mm (25%), respectively, with the individualized models compared to the population model over 17 selected OARs and a target of 39 patients. While the average improvement may seem mild, we found that the improvement for outlier patients with structures of large anatomical changes is significant. The number of patients with at least 0.05 DSC improvement or 2 mm HD95 improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture Voxelmorph, is 10 out of 39 test patients. By deriving the individualized model using TTO from the pre-trained population model, TTO models can be ready in about one minute. We also generated the adapted fractional models for each of the 39 test patients by progressively refining the individualized models using TTO to CBCT images acquired at later fractions of online ART treatment. When adapting the individualized model to a later fraction of the same patient, the model can be ready in less than a minute with slightly improved accuracy.
CONCLUSIONS : The proposed TTO method is well-suited for online ART and can boost segmentation accuracy for DL-based DIR models, especially for outlier patients where the pre-trained models fail. This article is protected by copyright. All rights reserved.
Liang Xiao, Chun Jaehee, Morgan Howard, Bai Ti, Nguyen Dan, Park Justin, Jiang Steve
2022-Oct-30
