Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging

Author:

Mareček Radek1ORCID,Říha Pavel12,Bartoňová Michaela12,Kojan Martin123,Lamoš Martin1,Gajdoš Martin1,Vojtíšek Lubomír1ORCID,Mikl Michal1ORCID,Bartoň Marek1,Doležalová Irena3,Pail Martin3,Strýček Ondřej123,Pažourková Marta3,Brázdil Milan13,Rektor Ivan13

Affiliation:

1. Central European Institute of Technology (CEITEC) Masaryk University Brno Czech Republic

2. Medical Faculty Masaryk University Brno Czech Republic

3. Brno Epilepsy Center, First Department of Neurology St. Anne's University Hospital and Medical Faculty of Masaryk University Brno Czech Republic

Funder

Agentura Pro Zdravotnický Výzkum České Republiky

Ministerstvo Školství, Mládeže a Tělovýchovy

Publisher

Wiley

Subject

Neurology (clinical),Neurology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Reference39 articles.

1. Ahmed B. Thesen T. Blackmon K. Zhao Y. Devinsky O. Kuzniecky R. &Brodley C. (2014).Hierarchical conditional random fields for outlier detection:An application to detecting epileptogenic cortical malformations. Paper Presented at the Proceedings of the 31st International Conference on Machine Learning Proceedings of Machine Learning Research. Retrieved fromhttp://proceedings.mlr.press

2. Unified segmentation

3. Unveiling epileptogenic lesions: The contribution of image processing

4. Magnetic resonance imaging pattern learning in temporal lobe epilepsy: Classification and prognostics

5. Altered Microstructure in Temporal Lobe Epilepsy: A Diffusional Kurtosis Imaging Study

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