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Multiclass segmentation of brain intraoperative ultrasound images with limited data

François-Xavier Carton 1, 2 Jack Noble 2 Florian Le Lann Bodil Munkvold Ingerid Reinertsen Matthieu Chabanas 1
1 TIMC-GMCAO - Gestes Medico-chirurgicaux Assistés par Ordinateur
TIMC - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
Abstract : During tumor resection surgery, intraoperative ultrasound images of the brain show anatomical structures like the sulci , falx cerebri and tentorium cerebelli , as well as the tumor. After resection started, the resection cavity is also visible. 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. In this work, we compare single-and multi-class segmentation models for the sulci , falx cerebri , tumor, resection cavity and ventricle. We present strategies to overcome the severe class imbalance in the training data, and train a model with limited 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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https://hal.archives-ouvertes.fr/hal-03185247
Contributeur : Matthieu Chabanas <>
Soumis le : mardi 30 mars 2021 - 11:57:43
Dernière modification le : jeudi 6 mai 2021 - 08:52:01

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François-Xavier Carton, Jack Noble, Florian Le Lann, Bodil Munkvold, Ingerid Reinertsen, et al.. Multiclass segmentation of brain intraoperative ultrasound images with limited data. SPIE Medical Imaging, Feb 2021, Online Only, France. pp.19, ⟨10.1117/12.2581861⟩. ⟨hal-03185247⟩

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