L
Lisa M. Koch
Researcher at ETH Zurich
Publications - 21
Citations - 2152
Lisa M. Koch is an academic researcher from ETH Zurich. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 12, co-authored 21 publications receiving 1311 citations. Previous affiliations of Lisa M. Koch include Imperial College London.
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.
Journal ArticleDOI
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Christian F. Baumgartner,Konstantinos Kamnitsas,Jacqueline Matthew,Tara P. Fletcher,Sandra Smith,Lisa M. Koch,Bernhard Kainz,Daniel Rueckert +7 more
TL;DR: In this paper, the authors proposed a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localization of the fetal structures via a bounding box.
Book ChapterDOI
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
TL;DR: In this article, a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images is presented.
Proceedings ArticleDOI
Visual Feature Attribution Using Wasserstein GANs
TL;DR: In this article, a feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN) was proposed for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment and Alzheimer's disease.
Posted Content
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
TL;DR: In this article, a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images is presented.