Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

no code implementations • 9 Aug 2021 • Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau

The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.

Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model

no code implementations • MIDL 2019 • Abdul Qayyum, Alain Lalande, Thomas Decourselle, Thibaut Pommier, Alexandre Cochet, Fabrice Meriaudeau

The proposed model could be used for the automatic segmentation of myocardial border that is a very important step for accurate quantification of no-reflow, myocardial infarction, myocarditis, and hypertrophic cardiomyopathy, among others.

LV Segmentation Segmentation