S
Sandy Engelhardt
Researcher at University Hospital Heidelberg
Publications - 87
Citations - 2311
Sandy Engelhardt is an academic researcher from University Hospital Heidelberg. The author has contributed to research in topics: Mitral valve & Computer science. The author has an hindex of 13, co-authored 70 publications receiving 1159 citations. Previous affiliations of Sandy Engelhardt include University of Mannheim & German Cancer Research Center.
Papers
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Journal ArticleDOI
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard,Alain Lalande,Clement Zotti,Frederick Cervenansky,Xin Yang,Pheng-Ann Heng,Irem Cetin,Karim Lekadir,Oscar Camara,Miguel Ángel González Ballester,Gerard Sanroma,Sandy Napel,Steffen E. Petersen,Georgios Tziritas,Elias Grinias,Mahendra Khened,Varghese Alex Kollerathu,Ganapathy Krishnamurthi,Marc-Michel Rohé,Xavier Pennec,Maxime Sermesant,Fabian Isensee,Paul F. Jäger,Klaus H. Maier-Hein,Peter M. Full,Ivo Wolf,Sandy Engelhardt,Christian F. Baumgartner,Lisa M. Koch,Jelmer M. Wolterink,Ivana Išgum,Yeonggul Jang,Yoonmi Hong,Jay Patravali,Shubham Jain,Olivier Humbert,Pierre-Marc Jodoin +36 more
TL;DR: 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 is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
Book ChapterDOI
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
Fabian Isensee,Paul F. Jaeger,Peter M. Full,Peter M. Full,Ivo Wolf,Sandy Engelhardt,Sandy Engelhardt,Klaus H. Maier-Hein +7 more
TL;DR: This paper uses an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium (LVM) on each time instance of the cardiac cycle to address named limitations of cardiac magnetic resonance imaging.
Journal ArticleDOI
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.
Zhaohan Xiong,Qing Xia,Zhiqiang Hu,Ning Huang,Cheng Bian,Yefeng Zheng,Sulaiman Vesal,Nishant Ravikumar,Andreas Maier,Xin Yang,Pheng-Ann Heng,Dong Ni,Caizi Li,Qianqian Tong,Weixin Si,Elodie Puybareau,Younes Khoudli,Thierry Géraud,Chen Chen,Wenjia Bai,Daniel Rueckert,Lingchao Xu,Xiahai Zhuang,Xinzhe Luo,Shuman Jia,Maxime Sermesant,Yashu Liu,Kuanquan Wang,Davide Borra,Alessandro Masci,Cristiana Corsi,Coen de Vente,Mitko Veta,Rashed Karim,Chandrakanth Jayachandran Preetha,Sandy Engelhardt,Menyun Qiao,Yuanyuan Wang,Qian Tao,Marta Nuñez-Garcia,Oscar Camara,Nicoló Savioli,Pablo Lamata,Jichao Zhao +43 more
TL;DR: This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.
Journal ArticleDOI
Machine Learning for Surgical Phase Recognition: A Systematic Review.
Carly R. Garrow,Karl-Friedrich Kowalewski,Linhong Li,Martin Wagner,Mona W. Schmidt,Sandy Engelhardt,Daniel A. Hashimoto,Hannes Kenngott,Sebastian Bodenstedt,Stefanie Speidel,Beat P. Müller-Stich,Felix Nickel +11 more
TL;DR: An overview of ML models and data streams utilized for automated surgical phase recognition can be performed with high accuracy, depending on the model, data type, and complexity of surgery.
BookDOI
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
Fabian Isensee,Paul F. Jaeger,Peter M. Full,Peter M. Full,Ivo Wolf,Sandy Engelhardt,Sandy Engelhardt,Klaus H. Maier-Hein +7 more
TL;DR: In this article, an ensemble of UNet-inspired architectures was used for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium (LVM) on each time instance of the cardiac cycle.