ESTRO 2024 - Abstract Book

S3123

Physics - Autosegmentation

ESTRO 2024

References:

1-Wang, Yong, Wei Xia, Baoyan Liu, Liu Zhou, Meng Ni, Rui Zhang, Jingyi Shen et al. "Exploration of spatial distribution of brain metastasis from small cell lung cancer and identification of metastatic risk level of brain regions: a multicenter, retrospective study." Cancer Imaging 21 (2021): 1-10. 2- Rudie, Jeffrey D., Rachit Saluja David A. Weiss, Pierre Nedelec, Evan Calabrese, John B. Colby, Benjamin Laguna, John Mongan et al. "The University of California San Francisco, Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset." arXiv preprint arXiv:2304.07248(2023). 3- Huang Y, Bert C, Sommer P, Frey B, Gaipl U, Distel LV, Weissmann T, Uder M, Schmidt MA, Dörfler A, Maier A, Fietkau R, Putz F. Deep learning for brain metastasis detection and segmentation in longitudinal MRI data. Medical Physics. 2022 Sep;49(9):5773-86. 4- Kao, Po-Yu, Thuyen Ngo, Angela Zhang, Jefferson W. Chen, and B. S. Manjunath. "Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, pp. 128-141. Springer International Publishing, 2019.

2664

Digital Poster

OARs auto-segmentation in cervical and prostate radiotherapy: evaluation of four automatic tools

Bianca Bordigoni 1 , Sara Trivellato 1 , Roberto Pellegrini 2 , Sofia Meregalli 3 , Elisa M Bonetto 3 , Maria Belmonte 4 , Marco Castellano 4 , Denis Panizza 1,4 , Martina C Daniotti 5,1 , Stefano Carminati 5,1 , Stefano Arcangeli 3,4 , Elena De Ponti 1,4 1 Fondazione IRCCS San Gerardo dei Tintori, Medical Physics, Monza, Italy. 2 Elekta AB, Medical Affairs, Stockholm, Sweden. 3 Fondazione IRCCS San Gerardo dei Tintori, Radiation Oncology, Monza, Italy. 4 University of Milano Bicocca, School of Medicine and Surgery, Milano, Italy. 5 University of Milano, School of Medical Physics, Milano, Italy

Purpose/Objective:

In this study, four automatic segmentation (AS) tools were evaluated in OAR contouring in cervical (CC) and prostate cancer (PC) treatment. The first one is an atlas-based model named Simultaneous Truth And Performance Level Evaluation (ST), the second is a conventional machine learning-based model as a Random Forest (RF), and the last 2 are commercial Deep Learning (DL)-based tools, MVision (MV) (v1.2.2, MVision AI, Helsinki, Finland) and LimbusAI (LI) (v1.7.0-B3, Limbus AI Inc, Regina, Canada).

Material/Methods: