Refer to the following citation for any use of the EMIDEC datasets:
Lalande, A.; Chen, Z.; Decourselle, T.; Qayyum, A.; Pommier, T.; Lorgis, L.; de la Rosa, E.; Cochet, A.; Cottin, Y.; Ginhac, D.; Salomon, M.; Couturier, R.; Meriaudeau, F. Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI. Data 2020, 5, 89. https://doi.org/10.3390/data5040089
The results of the Emidec challenge have been made public during the STACOM workshop, the 4th of October (9 :00-18 :00 UTC time) and are now available on the Leaderboard page.
Refer to the following citation for any comparison or discussion with the EMIDEC results:
Lalande, A.; Chen, Z.; Pommier, T.; et al. Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. Medical Image Analysis 2022, 79, 102428. https://doi.org/10.1016/j.media.2022.102428
Keywords: MRI, heart, myocardial infarction, segmentation, classification, delayed-enhancement
One crucial parameter to evaluate the state of the heart after myocardial infarction (MI) is the viability of the myocardial segment, i.e. if the segment recovers its functionality upon revascularization. MRI performed several minutes after the injection of a contrast agent (delayed enhancement-MRI or DE-MRI) is a method of choice to evaluate the extent of MI, and by extension, to assess viable tissues after an injury (in conjunction with the thickening of the muscle evaluated from cine-MRI).
The two main objectives of the EMIDEC challenge are first to classify normal and pathological cases from the clinical information with or without DE-MRI, and secondly to automatically detect the different relevant areas (the myocardial contours, the infarcted area and the permanent microvascular obstruction area (no-reflow area)) from a series of short-axis DE-MRI covering the left ventricle. The segmentation allows us to make a quantification of the MI, in absolute value (mm3) or percentage of the myocardium.
The database consists of 150 exams (all from different patients) 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. Along with MRI, clinical characteristics are provided to distinguish normal and pathological cases. The training set (100 cases) will be available mid-April.
To participate to the challenge and get access to the datasets, each participant should create an account . Once registered, the participant could access to the datasets of the challenge through its personal account.
The Segmentation contest will take place in September 2020, before the conference, and the global ranking will be based on geometrical and clinical metrics currently used in medical practices.
The Classification contest will happen during the MICCAI conference in October 2020, and the global ranking will correspond to the classification accuracy.
Moreover, the participants will also be requested to submit a paper following the MICCAI format that describe the methodology. The submitted papers will be accepted after a deep proofreading.
Task 1: Segmentation contest of the myocardium for all the DE-MRI exams, and the myocardial infarction and no-reflow areas on exams classified as pathologic ones. Contest done before the conference.
Task 2: Classification contest of the exams in normal or pathological one, according to the clinical data with or without the DE-MRI exams (two sub-challenges, the first one from only the clinical informations, and the second one considering the clinical informations and the DE-MRI). Contest done online and on-site
Registration and challenge:
Registration and challenge:
Primary contact person
ImVia Laboratory and University Hospital of Dijon, Dijon, FR
Other members of the team
Fabrice Meriaudeau, ImVia Laboratory,
Dominique Ginhac, ImVia Laboratory, Dijon, FR
Abdul Qayyum, ImVia Laboratory, Dijon, FR
Khawla Brahim, ImVia Laboratory, Dijon, FR
Thibaut Pommier, University Hospital of Dijon, Dijon, FR
Raphaël Couturier, Femto-St laboratory, Belfort, FR
Michel Salomon, Femto-St laboratory, Belfort, FR
Gilles Perrot, Femto-St laboratory, Belfort, FR
Zhihao Chen, Femto-St laboratory, Belfort, FR