Optimization of a Shape Metric Based on Information Theory Applied to Segmentation Fusion and Evaluation in Multimodal MRI for DIPG Tumor Analysis
| Autor: | Frédérique Frouin, Jacques Grill, Nathalie Boddaert, Régis Clouard, Stéphanie Jehan-Besson |
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| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Mutual information Information theory Joint entropy 030218 nuclear medicine & medical imaging Quantities of information 03 medical and health sciences 0302 clinical medicine Computer Science::Computer Vision and Pattern Recognition Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Medical imaging 020201 artificial intelligence & image processing Segmentation Shape optimization Artificial intelligence business |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030802080 GSI |
| DOI: | 10.1007/978-3-030-80209-7_83 |
| Popis: | In medical imaging, the construction of a reference shape from a set of segmentation results from different algorithms or image modalities is an important issue when dealing with the evaluation of segmentation without knowing the gold standard or when an evaluation of the inter or intra expert variability is needed. It is also interesting to build this consensus shape to merge the results obtained for the same target object from automatic or semi-automatic segmentation methods. In this paper, to deal with both segmentation fusion and evaluation, we propose to define such a “mutual shape” as the optimum of a criterion using both the mutual information and the joint entropy of the segmentation methods. This energy criterion is justified using the similarities between quantities of information theory and area measures and is presented in a continuous variational framework. We investigate the applicability of our framework for the fusion and evaluation of segmentation methods in multimodal MR images of diffuse intrinsic pontine glioma (DIPG). |
| Databáze: | OpenAIRE |
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