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

@article{Bernard2018DeepLT,
  title={Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?},
  author={Olivier Bernard and Alain Lalande and Cl{\'e}ment Zotti and Frederick Cervenansky and Xiaodong Yang and Pheng Ann Heng and Irem Cetin and Karim Lekadir and Oscar Camara and Miguel A. Gonz{\'a}lez Ballester and Gerard Sanroma and Sandy Napel and Steffen E. Petersen and Georgios Tziritas and Elias Grinias and Mahendra Khened and Varghese Alex Kollerathu and Ganapathy Krishnamurthi and Marc-Michel Roh{\'e} and Xavier Pennec and Maxime Sermesant and Fabian Isensee and Paul J{\"a}ger and Klaus H. Maier-Hein and Peter M. Full and Ivo Wolf and Sandy Engelhardt and Christian F. Baumgartner and Lisa M. Koch and Jelmer M. Wolterink and Ivana I{\vs}gum and Yeonggul Jang and Yoonmi Hong and Jay Patravali and Rupali Jagadale and Olivier Humbert and Pierre-Marc Jodoin},
  journal={IEEE Transactions on Medical Imaging},
  year={2018},
  volume={37},
  pages={2514-2525}
}
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… CONTINUE READING

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