Segmentation by Test-Time Optimization for CBCT-Based Adaptive Radiation Therapy
X Liang*, T Bai, J Chun, H Morgan, D Nguyen, J Park, S Jiang, University of Texas Southwestern Medical Center, Dallas, TX
Presentations
TH-B-206-4 (Thursday, 7/14/2022) 8:30 AM - 9:30 AM [Eastern Time (GMT-4)]
Room 206
Purpose: Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk in mostly 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 through traditional or deep learning (DL) based deformable image registration (DIR) can achieve improved results. Typical DL-based DIR models are population based, that is, trained with a dataset for a population of patients, so they may be affected by the generalizability problem.
Methods: 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 number of patients with at least 0.05 Dice Similarity Coefficient improvement or 2 mm 95% Hausdorff Distance improvement by TTO averaged over the 17 selected structures for the state-of-the-art architecture Voxelmorph is 10 out of 39 test patients. The average time for deriving the individualized model using TTO from the pre-trained population model is approximately four minutes.
Conclusion: 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.
Keywords
Cone-beam CT, Segmentation, Deformation
Taxonomy
IM/TH- Image Segmentation Techniques: Machine Learning
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