OPTIMIZED MULTI-ATLAS PROSTATE SEGMENTATION FROM 3D CT IMAGES

Abstract : The purpose of this study was to evaluate and optimize the performance of a multi-atlas based method for the segmentation of prostate in CT scans improving it up to the limit of the inter-observer variability. We assessed and optimized the atlas selection, the Non-Rigid Registration (NRR) and the label fusion steps by introducing new similarity measures based on image features and a multi-scale weighted majority voting. Cross validation results on 45 CT images suggested that the similarity measure based on the local feature histogram of oriented gradients outperformed classical intensity-based metrics for atlas selection. Besides, the NiftyReg optimized in a region of interest was found to be the optimal NRR algorithm. For the label fusion, the multi-scale weighted majority voting outperformed other approaches. All those improvements led to Dice scores of 0.84 +/- 0.03, which are comparable to the inter-observer variability for manual contouring.
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Conference papers
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02302409
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, October 1, 2019 - 2:58:46 PM
Last modification on : Wednesday, October 2, 2019 - 1:26:21 AM

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  • HAL Id : hal-02302409, version 1

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Yitian Zhou, Laurent Launay, Julien Bert, Renaud de Crevoisier, Oscar Acosta. OPTIMIZED MULTI-ATLAS PROSTATE SEGMENTATION FROM 3D CT IMAGES. 16th IEEE International Symposium on Biomedical Imaging (ISBI), Apr 2019, Venice, Italy. ⟨hal-02302409⟩

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