Abstract
Segmentation of Organs at Risk is a fundamental step during radiotherapy planning for cancer treatment. Its goal is to preserve healthy tissue around the tumor and ensure that the most radiation strikes only cancer cells. Physicians do this job manually, which can be slow and error-prone. Thus, automatic segmentation methodologies can speed up organ delimiting during radiotherapy planning. This work designs a method, EfficientDeepLab, a convolutional neural network architecture trained on CT scans for trachea segmentation, and obtained an 88.6% dice score.
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Acknowledgments
This work was carried out with the support of the Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) - Financing Code 001, Maranhão Research Support Foundation (FAPEMA), National Council for Scientific and Technological Development (CNPq) and Brazilian Company of Hospital Services (Ebserh) Brazil (Proc. 409593/2021-4).
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Fernandes, A.G.S., Braz Junior, G., Diniz, J.O.B., Silva, A.C., Matos, C.E.F. (2023). EfficientDeepLab for Automated Trachea Segmentation on Medical Images. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_11
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