Rüfenacht, Elias

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Rüfenacht, Elias; Kamath, Amith; Suter, Yannick; Poel, Robert; Ermiş, Ekin; Scheib, Stefan; Reyes, Mauricio (2023). PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion. Computer methods and programs in biomedicine, 231(107374), p. 107374. Elsevier 10.1016/j.cmpb.2023.107374

Rüfenacht, Elias; Poel, Robert; Kamath, Amith; Ermis, Ekin; Scheib, Stefan; Fix, Michael K.; Reyes, Mauricio (2023). Dose Guidance for Radiotherapy-Oriented Deep Learning Segmentation. Lecture notes in computer science, 14228, pp. 525-534. Cham: Springer 10.1007/978-3-031-43996-4_50


Poel, Robert; Rüfenacht, Elias; Ermis, Ekin; Müller, Michael; Fix, Michael K; Aebersold, Daniel M; Manser, Peter; Reyes, Mauricio (2022). Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma. Radiation oncology, 17(1), p. 170. BioMed Central 10.1186/s13014-022-02137-9


Poel, Robert; Rüfenacht, Elias; Herrmann, Evelyn; Scheib, Stefan; Manser, Peter; Aebersold, Daniel M.; Reyes, Mauricio (2021). The predictive value of segmentation metrics on dosimetry in organs at risk of the brain. Medical image analysis, 73, p. 102161. Elsevier 10.1016/j.media.2021.102161


Rüfenacht, Elias; Jungo, Alain; Ermis, Ekin; Hemmatazad, Hossein; Blatti, Marcela Judith; Aebersold, Daniel; Manser, Peter; Fix, Michael; Reyes, Mauricio; Herrmann, Evelyn (December 2019). Fully automated organs at risk delineation for brain tumor radiation planning in patients with glioblastoma using deep learning. Strahlentherapie und Onkologie, 195(12), p. 1149. Berlin Heidelberg: Springer

Rüfenacht, Elias; Jungo, Alain; Ermis, Ekin; Blatti-Moreno, Marcela; Hemmatazad, Hossein; Aebersold, Daniel M.; Fix, Michael; Manser, Peter; Reyes, Mauricio; Herrmann, Evelyn (2019). Organs at Risk Delineation for Brain Tumor Radiation Planning in Patients with Glioblastoma Using Deep Learning. International journal of radiation oncology, biology, physics, 105(1), E718-E719. Elsevier 10.1016/j.ijrobp.2019.06.892

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