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Conference papers

Unsupervised Quality Control of Image Segmentation based on Bayesian Learning

Abstract : Assessing the quality of segmentations on an image database is required as many downstream clinical applications are based on segmentation results. For large databases, this quality assessment becomes tedious for a human expert and therefore some automation of this task is necessary. In this paper, we introduce a novel unsupervised approach to assist the quality control of image segmentations by measuring their adequacy with segmentations produced by a generic probabilistic model. To this end, we introduce a new segmentation model combining intensity and a spatial prior %which enforces the smoothness of the prior label probability defined through a combination of spatially smooth kernels. The tractability of the approach is obtained by solving a type-II maximum likelihood which directly estimates hyperparameters. Assessing the quality of the segmentation with respect to the probabilistic model allows to detect the most challenging cases inside a dataset. This approach was evaluated on the BRATS 2017 and ACDC datasets showing its relevance for quality control assessment.
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Contributor : Benoît Audelan Connect in order to contact the contributor
Submitted on : Thursday, August 8, 2019 - 3:24:27 PM
Last modification on : Friday, February 4, 2022 - 3:12:41 AM
Long-term archiving on: : Thursday, January 9, 2020 - 9:44:47 AM


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  • HAL Id : hal-02265131, version 1


Benoît Audelan, Hervé Delingette. Unsupervised Quality Control of Image Segmentation based on Bayesian Learning. MICCAI 2019 - 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2019, Shenzhen, China. ⟨hal-02265131⟩



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