TY - JOUR
T1 - Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis
T2 - Is the Problem Solved?
AU - Bernard, Olivier
AU - Lalande, Alain
AU - Zotti, Clement
AU - Cervenansky, Frederick
AU - Yang, Xin
AU - Heng, Pheng Ann
AU - Cetin, Irem
AU - Lekadir, Karim
AU - Camara, Oscar
AU - Gonzalez Ballester, Miguel Angel
AU - Sanroma, Gerard
AU - Napel, Sandy
AU - Petersen, Steffen
AU - Tziritas, Georgios
AU - Grinias, Elias
AU - Khened, Mahendra
AU - Kollerathu, Varghese Alex
AU - Krishnamurthi, Ganapathy
AU - Rohe, Marc Michel
AU - Pennec, Xavier
AU - Sermesant, Maxime
AU - Isensee, Fabian
AU - Jager, Paul
AU - Maier-Hein, Klaus H.
AU - Full, Peter M.
AU - Wolf, Ivo
AU - Engelhardt, Sandy
AU - Baumgartner, Christian F.
AU - Koch, Lisa M.
AU - Wolterink, Jelmer M.
AU - Isgum, Ivana
AU - Jang, Yeonggul
AU - Hong, Yoonmi
AU - Patravali, Jay
AU - Jain, Shubham
AU - Humbert, Olivier
AU - Jodoin, Pierre Marc
PY - 2018/11
Y1 - 2018/11
N2 - Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the 'Automatic Cardiac Diagnosis Challenge' dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
AB - Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the 'Automatic Cardiac Diagnosis Challenge' dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
KW - Cardiac segmentation and diagnosis
KW - Deep learning
KW - Left and right ventricles
KW - MRI
KW - Myocardium
UR - http://www.scopus.com/inward/record.url?scp=85047000847&partnerID=8YFLogxK
U2 - 10.1109/TMI.2018.2837502
DO - 10.1109/TMI.2018.2837502
M3 - Article
C2 - 29994302
AN - SCOPUS:85047000847
SN - 0278-0062
VL - 37
SP - 2514
EP - 2525
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 11
M1 - 8360453
ER -