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  Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics

Thierbach, K., Bazin, P.-L., de Back, W., Gavriilidis, F., Kirilina, E., Jäger, C., et al. (2018). Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics. In Machine Learning in Medical Imaging 2018 (pp. 179-187). Cham: Springer. doi:10.1007/978-3-030-00919-9_21.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-60B8-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-BFD7-4
Genre: Conference Paper

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 Creators:
Thierbach, Konstantin1, Author              
Bazin, Pierre-Louis1, 2, Author              
de Back, Walter 3, Author
Gavriilidis, Filippos1, Author              
Kirilina, Evgeniya1, 4, Author              
Jäger, Carsten1, Author              
Morawski, Markus 5, Author
Geyer, Stefan1, Author              
Weiskopf, Nikolaus1, Author              
Scherf, Nico1, Author              
Affiliations:
1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205649              
2University of Amsterdam, the Netherlands, ou_persistent22              
3Institute for Medical Informatics and Biometry, University Hospital Carl Gustav Carus, Dresden, Germany, ou_persistent22              
4Center for Cognitive Neuroscience Berlin (CCNB), FU Berlin, Germany, ou_persistent22              
5Paul Flechsig Institute for Brain Research, University of Leipzig, Germany, ou_persistent22              

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Free keywords: Histology; Image segmentation; Cell detection; Deep learning; Convolutional neural networks; Active contours
 Abstract: Deep learning has thoroughly changed the field of image analysis yielding impressive results whenever enough annotated data can be gathered. While partial annotation can be very fast, manual segmentation of 3D biological structures is tedious and error-prone. Additionally, high-level shape concepts such as topology or boundary smoothness are hard if not impossible to encode in Feedforward Neural Networks. Here we present a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models. We show that the network can be trained efficiently from simple cell centroid annotations, and that the final segmentation provides accurate cell detection and smooth segmentations that do not introduce further cell splitting or merging.

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Language(s): eng - English
 Dates: 2018-09-152018
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1007/978-3-030-00919-9_21
 Degree: -

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Title: 9th International Workshop on Machine Learning in Medical Imaging
Place of Event: Granada, Spain
Start-/End Date: 2018-09-16 - 2018-09-16

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Project name : Non-invasive in vivo histology in health and disease using Magnetic Resonance Imaging (MRI) / HMRI
Grant ID : 616905
Funding program : FP7 (ERC-2013-CoG)
Funding organization : European Research Council

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Title: Machine Learning in Medical Imaging 2018
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Cham : Springer
Pages: 409 Volume / Issue: - Sequence Number: - Start / End Page: 179 - 187 Identifier: ISBN: 978-303000918-2