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Shape Analysis for Brain Structures

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Shape Analysis in Medical Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 14))

Abstract

Advances in magnetic resonance imaging (MRI) have enabled non-invasive examination of brain structures in unprecedented details. With increasing amount of high resolution MRI data becoming available, we are at a position to make significant clinical contributions. In this chapter, we review the main approaches to shape analysis for brain structures. The purpose of this review is to provide methodological insights for pushing forward shape analysis research, so that we can better benefit from the available high resolution data. We describe in this review point-based, mesh-based, function-based, and medial representations as well as deformetrics. Their respective advantages and disadvantages as well as the implications of increasing resolution and greater sample sizes on these shape analysis approaches are discussed.

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Authors and Affiliations

  1. PARIETAL Team, INRIA, Gif-Sur-Yvette, France

    Bernard Ng

  2. FIND Lab, Stanford University, Stanford, CA, USA

    Bernard Ng

  3. Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Matthew Toews

  4. ARAMIS Team, INRIA, Paris, France

    Stanley Durrleman

  5. Institut du Cerveau et de la Moëlle épinière (ICM), Hôpital de la Pitié Salpêtrière, Paris, France

    Stanley Durrleman

  6. Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, LosAngeles, CA, USA

    Yonggang Shi

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  4. Yonggang Shi
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Correspondence to Bernard Ng .

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  1. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Ng, B., Toews, M., Durrleman, S., Shi, Y. (2014). Shape Analysis for Brain Structures. In: Li, S., Tavares, J. (eds) Shape Analysis in Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-03813-1_1

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