Abstract
Accurate dose map prediction is key to external radiotherapy. Previous methods have achieved promising results; however, most of these methods learn the dose map as a black box without considering the beam-shaped radiation for treatment delivery in clinical practice. The accuracy is usually limited, especially on beam paths. To address this problem, this paper describes a novel “disassembling-then-assembling" strategy to consider the dose prediction task from the nature of radiotherapy. Specifically, a global-to-beam network is designed to first predict dose values of the whole image space and then utilize the proposed innovative beam masks to decompose the dose map into multiple beam-based sub-fractions in a beam-wise manner. This can disassemble the difficult task to a few easy-to-learn tasks. Furthermore, to better capture the dose distribution in region-of-interest (ROI), we introduce two novel value-based and criteria-based dose volume histogram (DVH) losses to supervise the framework. Experimental results on the public OpenKBP challenge dataset show that our method outperforms the state-of-the-art methods, especially on beam paths, creating a trustable and interpretable AI solution for radiotherapy treatment planning. Our code is available at https://github.com/ukaukaaaa/BeamDosePrediction.
B. Wang and L. Teng—Equal Contribution
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Wang, B. et al. (2022). Deep Learning-Based Head and Neck Radiotherapy Planning Dose Prediction via Beam-Wise Dose Decomposition. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13437. Springer, Cham. https://doi.org/10.1007/978-3-031-16449-1_55
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