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Knowledge-Driven Recognition and Segmentation of Internal Brain Structures in 3D MRI

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Computational Surgery and Dual Training

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Abstract

The complexity of the human body and of its understanding through medical imaging techniques requires the development of image processing and interpretation methods that cope with this complexity. Recently, significant advances both in image acquisition techniques and in image processing have been performed. In particular, advanced and sophisticated image processing methods find in medical imaging a privileged field of applications. The major objectives concern help to diagnosis, therapy planning, surgical planning, patient’s follow-up, morphometry, variability assessment, modeling, support for neuroscience applications, etc. This covers both clinical and research applications. Toward these aims, methods have to be developed to improve image quality, to perform segmentation and recognition of organs, pathologies, etc., to provide quantitative measures, to fuse multimodal image data, to provide numerical models, and to improve 3D visualization.

In this chapter, we summarize some of the work of our group on brain image interpretation, focusing on knowledge representation and its use to guide recognition and segmentation of internal brain structures in 3D MRI data. Instead of using shape models [1–3] or digital anatomical atlases [4–6], we rely on an explicit representation of structural information expressed as spatial relations between brain structures.

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Acknowledgements

The work described in this chapter is mostly joint work with several colleagues, including Elsa Angelini, Jamal Atif, Bénédicte Batrancourt, Olivier Colliot [43], Geoffroy Fouquier, Thierry Géraud [44], Céline Hudelot, Vincent Israel-Jost, Hassan Khotanlou [45], Olivier Nempont [42], Aymeric Perchant [46], Nathalie Richard. It has been performed in collaboration with several hospitals in Paris (Pitié-Salpétriére, Sainte-Anne, Saint Vincent de Paul) and laboratories (LENA-CNRS, LIMSI-CNRS, ENST Bretagne, IFR 49, universities of Sao Paulo, Bogota, Columbia…). It benefited from funding from Région Ile-de-France, ParisTech, INCA, ANR, Institut TELECOM…

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  1. Telecom ParisTech, CNRS UMR 5141 LTCI, Paris, France

    Isabelle Bloch

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  1. Isabelle Bloch
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Correspondence to Isabelle Bloch .

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Editors and Affiliations

  1. Dept. Computer Science, University of Houston, Calhoun Rd. 4800, Houston, 77204-3475, U.S.A.

    Marc Garbey

  2. Methodist Hospital Research Institute, Fannin St. 6565, Houston, 77030, U.S.A.

    Barbara Lee Bass

  3. ENSPS, Université Strasbourg I, boulevard Sébastien Brant, Illkirch, 67412, France

    Christophe Collet

  4. ENSPS, Université Louis Pasteur -Strasbourg I, Pôle API Boulevard. Sébastien Brant, Illkirch, 67400, France

    Michel Mathelin

  5. Dept. Mechanical & Aerospace Engineering, University of Florida, Gainesville, 32611-6250, U.S.A.

    Roger Tran-Son-Tay

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Bloch, I. (2010). Knowledge-Driven Recognition and Segmentation of Internal Brain Structures in 3D MRI. In: Garbey, M., Bass, B., Collet, C., Mathelin, M., Tran-Son-Tay, R. (eds) Computational Surgery and Dual Training. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1123-0_4

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  • DOI: https://doi.org/10.1007/978-1-4419-1123-0_4

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  • Online ISBN: 978-1-4419-1123-0

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