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Jointly Segmenting Prostate Zones in 3D MRIs by Globally Optimized Coupled Level-Sets

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8081))

Abstract

It is of great interest in image-guided prostate interventions and diagnosis of prostate cancer to accurately and efficiently delineate the boundaries of prostate, especially its two clinically meaningful sub-regions/zones of the central gland (CZ) and the peripheral zone (PZ), in the given magnetic resonance (MR) images. We propose a novel coupled level-sets/contours evolution approach to simultaneously locating the prostate region and its two sub-regions, which properly introduces the recently developed convex relaxation technique to jointly evolve two coupled level-sets in a global optimization manner. Especially, in contrast to the classical level-set methods, we demonstrate that the two coupled level-sets can be simultaneously moved to their globally optimal positions at each discrete time-frame while preserving the spatial inter-surface consistency; we study the resulting complicated combinatorial optimization problem at each discrete time evolution by means of convex relaxation and show its global and exact optimality, for which we introduce the novel coupled continuous max-flow model and demonstrate its duality to the investigated convex relaxed optimization problem with the region constraint. The proposed coupled continuous max-flow model naturally leads to a new and efficient algorithm, which enjoys great advantages in numerics and can be readily implemented on GPUs. Experiments over 10 T2-weighted 3D prostate MRIs, by inter- and intra-operator variability, demonstrate the promising performance of the proposed approach.

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Author information

Authors and Affiliations

  1. Medical Imaging Labs, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada, N6A 5K8

    Jing Yuan, Eranga Ukwatta, Wu Qiu, Martin Rajchl, Yue Sun & Aaron Fenster

  2. Mathematics Department, University of Bergen, Bergen, Norway

    Xue-Cheng Tai

Authors
  1. Jing Yuan
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  2. Eranga Ukwatta
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  3. Wu Qiu
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  4. Martin Rajchl
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  5. Yue Sun
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  6. Xue-Cheng Tai
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  7. Aaron Fenster
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Editor information

Editors and Affiliations

  1. Centre for Mathematical Sciences, Lund University, Sweden

    Anders Heyden , Fredrik Kahl , Carl Olsson  & Magnus Oskarsson , ,  & 

  2. Dept. of Mathematics, University of Bergen, Johaness Brunsgate 12, 5007, Bergen, Norway

    Xue-Cheng Tai

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Yuan, J. et al. (2013). Jointly Segmenting Prostate Zones in 3D MRIs by Globally Optimized Coupled Level-Sets. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, XC. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2013. Lecture Notes in Computer Science, vol 8081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40395-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-40395-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40394-1

  • Online ISBN: 978-3-642-40395-8

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