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Image Segmentation and Level Set Method: Application to Anatomical Head Model Creation

Jérome Piovano 1
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Magnetic Resonance Images (MRI) have been introduced at the end of the XXth century and have revolutionized the world of modern medicine, allowing to view with precision the inside of anatomical structures in a non-invasive way. This imaging technique has greatly contributed to the study and comprehension of the human brain, allowing to discern with precision the different anatomical structures composing the head, especially the cerebral cortex. Discernment between these anatomical structures is called segmentation, and consists in “extracting” structures of interest from MRIs. Several models exists to perform image segmentation, and this thesis focus on those based on hypersurface evolutions: an hypersurface (surface in 3D) is incrementally adjusted to finally fit the border of the region of interest. A head model corresponds to the partitioning of the head into several segmented anatomical structures. A classic head model generally includes 5 anatomical structures (skin, skull, cerebrospinal fluid, grey matter, white matter), nested inside each other in the manner of “Russian nested dolls”. Nevertheless because of the complexity of their shapes, manual segmentation of these structures is tedious and extremely difficult. This thesis is dedicated to the creation of new segmentation models robust to MRI alterations, and to the application of these models in the purpose of automatic creation of anatomical head models. After briefly reviewing some classical models in image segmentation, two contributions to segmentation based on hypersurface evolution are proposed. The first one corresponds to a new representation and a new numerical scheme for the level-sets method, based on quadrilateral finite elements. This representation aims at improving the accuracy and robustness of the model. The second contribution corresponds to a new segmentation model based on local statistics, and robust to standard MRI alterations. This model aims at unifying several ’state-of-the-art’ models in image segmentation. Finally, a framework for automatic creation of anatomical head models is proposed, mainly using the previous local-statistic based segmentation model.
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Submitted on : Monday, October 11, 2021 - 6:07:44 PM
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Jérome Piovano. Image Segmentation and Level Set Method: Application to Anatomical Head Model Creation. Computer Vision and Pattern Recognition [cs.CV]. Université de Nice-Sophia Antipolis, 2009. English. ⟨tel-03374002⟩

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