Abstract
Radiation therapy has been widely used in the treatment of cancer. However, a high-quality radiotherapy plan often requires dosimetrists to tweak repeatedly in a trial-and-error manner based on experience, causing it quite time-consuming and subjective. In this paper, we present a multi-task dose prediction (MTDP) network to automatically predict the dose distribution from computer tomography (CT) image. Specifically, the MTDP network consists of three highly-related tasks: a main dose prediction task for generating fine-grained dose value for each pixel, an auxiliary isodose lines prediction task for providing coarse-grained dose range for each pixel, and an auxiliary gradient prediction task for capturing subtle gradient information such as radiation patterns and edges of the dose distribution map, to obtain a more accurate and robust dose distribution map. The three related tasks are integrated via a shared encoder, following the multi-task learning strategy. To strengthen the correlations of different tasks, we also introduce two additional constraints, i.e., isodose consistency loss and gradient consistency loss, to enforce the match between the dose distribution features produced by the two auxiliary tasks and the main task. The experiments conducted on an in-house dataset with 110 rectum cancer patients have demonstrated the effectiveness and superiority of our method compared with the state-of-the-art methods. Code is available at https://github.com/DeepMedLab/MTDP-network.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (NSFC 62071314) and Sichuan Science and Technology Program (2021YFG0326, 2020YFG0079).
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Tan, S. et al. (2021). Incorporating Isodose Lines and Gradient Information via Multi-task Learning for Dose Prediction in Radiotherapy. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_71
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DOI: https://doi.org/10.1007/978-3-030-87234-2_71
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