Abstract
In radiation therapy (RTx), an accurate delineation of the regions of interest and organs at risk allows for a more targeted irradiation with reduced side effects. In the case of prostate cancer treatments, RTx planning requires the delineation of many pelvic structures. This is a time-consuming task and clinicians would greatly benefit from using robust automatic multi-structure segmentation tools.With the final purpose of introducing an automatic segmentation algorithm in clinical practice, we first address the problem of multi-structure segmentation in pelvic MR using a publicly available dataset. Moreover, we evaluate three types of preprocessing approaches to enable training and inference using different MR sequences. Despite a limited number of training samples, we report an average Dice score of 84.7 ± 10.2% in the segmentation of 8 pelvic structures. The code and the trained models are available at: https://github.com/FrancescaDB/multi_structure_segmentation_gold_atlas
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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De Benetti, F., Bogoi, S., Navab, N., Wendler, T. (2024). Preprocessing Evaluation and Benchmark for Multi-structure Segmentation of the Male Pelvis in MRI on the Gold Atlas Dataset. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_73
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DOI: https://doi.org/10.1007/978-3-658-44037-4_73
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