C
Christian Wachinger
Researcher at Ludwig Maximilian University of Munich
Publications - 158
Citations - 5342
Christian Wachinger is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 29, co-authored 143 publications receiving 3495 citations. Previous affiliations of Christian Wachinger include Massachusetts Institute of Technology & Siemens.
Papers
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Journal ArticleDOI
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks.
Abhijit Guha Roy,Sailesh Conjeti,Sri Phani Krishna Karri,Debdoot Sheet,Amin Katouzian,Christian Wachinger,Nassir Navab +6 more
TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
Book ChapterDOI
Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks
TL;DR: In this paper, three variants of squeeze and excitation (SE) modules are introduced for image segmentation, i.e., squeezing spatially and exciting channel-wise (cSE), squeezing channelwise and exciting spatially (sSE), and concurrent spatial and channel squeeze & excitation(scSE).
Journal ArticleDOI
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.
TL;DR: DeepNAT is an end‐to‐end learning‐based approach to brain segmentation that jointly learns an abstract feature representation and a multi‐class classification and the results show that DeepNAT compares favorably to state‐of‐the‐art methods.
Journal ArticleDOI
Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks
TL;DR: This paper effectively incorporate the recently proposed “squeeze and excitation” (SE) modules for channel recalibration for image classification in three state-of-the-art F-CNNs and demonstrates a consistent improvement of segmentation accuracy on three challenging benchmark datasets.
Journal ArticleDOI
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge
Esther E. Bron,Marion Smits,Wiesje M. van der Flier,Hugo Vrenken,Frederik Barkhof,Philip Scheltens,Janne M. Papma,Rebecca M. E. Steketee,Carolina Méndez Orellana,Rozanna Meijboom,Madalena Pinto,Joana R. Meireles,Carolina Garrett,António J. Bastos-Leite,Ahmed Abdulkadir,Olaf Ronneberger,Nicola Amoroso,Roberto Bellotti,David Cárdenas-Peña,Andrés Marino Álvarez-Meza,Chester V. Dolph,Khan M. Iftekharuddin,Simon Fristed Eskildsen,Pierrick Coupé,Vladimir S. Fonov,Katja Franke,Christian Gaser,Christian Ledig,Ricardo Guerrero,Tong Tong,Katherine R. Gray,Elaheh Moradi,Jussi Tohka,Alexandre Routier,Stanley Durrleman,Alessia Sarica,Giuseppe Di Fatta,Francesco Sensi,Andrea Chincarini,Garry Smith,Zhivko Stoyanov,Lauge Sørensen,Mads Nielsen,Sabina Tangaro,Paolo Inglese,Christian Wachinger,Martin Reuter,John C. van Swieten,Wiro J. Niessen,Stefan Klein +49 more
TL;DR: A grand challenge to objectively compare algorithms based on a clinically representative multi-center data set of three diagnostic groups, finding the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity.