Abstract
Careful delineation of normal-tissue organs-at-risk is essential for brain tumor radiotherapy. However, this process is time-consuming and subject to variability. In this work, we propose a multi-modality framework that automatically segments eleven structures. Large structures used for defining the clinical target volume (CTV), such as the cerebellum, are directly segmented from T1-weighted and T2-weighted MR images. Smaller structures used in radiotherapy plan optimization are more difficult to segment, thus, a region of interest is first identified and cropped by a classification model, and then these structures are segmented from the new volume. This bi-directional framework allows for rapid model segmentation and good performance on a standardized challenge dataset when evaluated with volumetric and surface metrics.
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Acknowledgements
The authors acknowledge the support of the High Performance Computing facility at the University of Texas MD Anderson Cancer Center and the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing computational resources that have contributed to the research results reported in this paper.
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Gay, S.S. et al. (2021). A Bi-directional, Multi-modality Framework for Segmentation of Brain Structures. In: Shusharina, N., Heinrich, M.P., Huang, R. (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science(), vol 12587. Springer, Cham. https://doi.org/10.1007/978-3-030-71827-5_6
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