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Joint segmentation via patient-specific latent anatomy model

Abstract : We present a generative approach for joint 3D segmentation of patient-speciØc MR scans across diÆerent modalities or time points. The latent anatomy, in the form of spatial parameters, is inferred si- multaneously with the evolution of the segmentations. The individual segmentation of each scan supports the segmentation of the group by sharing common information. The joint segmentation problem is solved via a statistically driven level-set framework. We illustrate the method on an example application of multimodal and longitudinal brain tumor segmentation, reporting promising segmentation results
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Submitted on : Friday, August 19, 2011 - 7:47:05 PM
Last modification on : Friday, November 18, 2022 - 9:25:48 AM


  • HAL Id : inria-00616174, version 1



Tammy Riklin-Raviv, Bjoern H. Menze, Koen van Leemput, Bram Stieltjes, Marc-André Weber, et al.. Joint segmentation via patient-specific latent anatomy model. MICCAI Workshop on Probabilistic Methods in Medical Image Analysis (PMMIA'09)), 2009, London, UK, United Kingdom. ⟨inria-00616174⟩



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