Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard (1) , Alain Lalande (2) , Clement Zotti (3) , Frederic Cervenansky (4) , Xin Yang (5) , Pheng-Ann Heng (5) , Irem Cetin (6) , Karim Lekadir (6) , Oscar Camara (6) , Miguel Angel Gonzalez Ballester (7) , Gerard Sanroma (6) , Sandy Napel (8) , Steffen Petersen (9) , Georgios Tziritas (10) , Elias Grinias (10) , Mahendra Khened (11) , Varghese Alex Kollerathu (11) , Ganapathy Krishnamurthi (11) , Marc-Michel Rohé (12, 13) , Xavier Pennec (12, 13) , Maxime Sermesant (12, 13) , Fabian Isensee (14) , Paul Jager (14) , Klaus H Maier-Hein (14) , Peter M. Full (15) , Ivo Wolf (16) , Sandy Engelhardt (16) , Chrisitan Baumgartner (17) , Lisa Koch (18) , Jelmer Wolterink (19) , Ivana Isgum (19) , Yeonggul Jang (20) , Yoonmi Hong (20) , Jay Patravali (21) , Shubham Jain (21) , Olivier Humbert (22) , Pierre-Marc Jodoin (3)

1 Images et Modèles
2 Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508]
3 UdeS - Département d'informatique [Sherbrooke]
4 Service Informatique et développements
5 CUHK - The Chinese University of Hong Kong [Hong Kong]
6 UPF - Universitat Pompeu Fabra [Barcelona]
7 ICREA - Institució Catalana de Recerca i Estudis Avançats = Catalan Institution for Research and Advanced Studies
8 Stanford School of Medicine [Stanford]
9 William Harvey Research Institute, Barts and the London Medical School
10 CSD-UOC - Computer Science Department [Crete]
11 Department of Engineering Design [Madras]
12 ASCLEPIOS - Analysis and Simulation of Biomedical Images
13 EPIONE - E-Patient : Images, données & mOdèles pour la médeciNe numériquE
14 DKFZ - German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg]
15 Heidelberg University Hospital [Heidelberg]
16 Hochschule Mannheim - University of Applied Sciences
17 Computer Vision Laboratory - ETHZ [Zurich]
18 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
19 University Medical Center [Utrecht]
20 Yonsei University
21 Qure.ai company
22 TIRO-MATOs UMR E4320 - Transporteurs et Imagerie, Radiothérapie en Oncologie et Mécanismes biologiques des Altérations du Tissu Osseux

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 classificationtask. 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.

Mots clés: MRI; Lleft and right ventricles; Cardiac segmentation and diagnosis; Myocardium; Deep learning

Domaines: Sciences du Vivant [q-bio]

Olivier Bernard, Alain Lalande, Clement Zotti, Frederic Cervenansky, Xin Yang, et al.. Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?. IEEE Transactions on Medical Imaging, 2018, 37 (11), pp.2514-2525. ⟨10.1109/TMI.2018.2837502⟩. ⟨hal-01803621⟩