Multiclass segmentation of brain intraoperative ultrasound images with limited data

François-Xavier Carton; Jack H. Noble; Florian Le Lann; Bodil K. R. Munkvold; Ingerid Reinertsen; Matthieu Chabanas

doi:10.1117/12.2581861

15 February 2021 Multiclass segmentation of brain intraoperative ultrasound images with limited data

François-Xavier Carton, Jack H. Noble, Florian Le Lann, Bodil K. R. Munkvold, Ingerid Reinertsen, Matthieu Chabanas

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115980M (2021) https://doi.org/10.1117/12.2581861
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

During tumor resection surgery, intraoperative ultrasound images of the brain show anatomical structures, the tumor, and the resection cavity (after resection started). These elements help with the localization and tumor resection, and can be used to register the preoperative MRI to intraoperative images, to compensate for the tissue deformation occurring during surgery. We evaluate a multi-class segmentation model for the sulci, falx cerebri, tumor, resection cavity and ventricle. We present strategies to overcome the severe class imbalance in the training data. We show that a multi-class model may leverage inter-class spatial relationships and produce more accurate results than single-class models.

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Citation Download Citation

François-Xavier Carton, Jack H. Noble, Florian Le Lann, Bodil K. R. Munkvold, Ingerid Reinertsen, and Matthieu Chabanas "Multiclass segmentation of brain intraoperative ultrasound images with limited data", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980M (15 February 2021); https://doi.org/10.1117/12.2581861

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