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  4. Learning to Segment 3D Linear Structures Using Only 2D Annotations
 
conference paper

Learning to Segment 3D Linear Structures Using Only 2D Annotations

Kozinski, Mateusz
•
Mosinska, Agata Justyna  
•
Salzmann, Mathieu  
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January 1, 2018
Medical Image Computing And Computer Assisted Intervention - Miccai 2018, Pt Ii 8560_
MICCAI

We propose a loss function for training a Deep Neural Network (DNN) to segment volumetric data, that accommodates ground truth annotations of 2D projections of the training volumes, instead of annotations of the 3D volumes themselves. In consequence, we significantly decrease the amount of annotations needed for a given training set. We apply the proposed loss to train DNNs for segmentation of vascular and neural networks in microscopy images and demonstrate only a marginal accuracy loss associated to the significant reduction of the annotation effort. The lower labor cost of deploying DNNs, brought in by our method, can contribute to a wide adoption of these techniques for analysis of 3D images of linear structures.

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Type
conference paper
DOI
10.1007/978-3-030-00934-2_32
Web of Science ID

WOS:000477921700032

Author(s)
Kozinski, Mateusz
Mosinska, Agata Justyna  
Salzmann, Mathieu  
Fua, Pascal  
Date Issued

2018-01-01

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG

Publisher place

Cham

Published in
Medical Image Computing And Computer Assisted Intervention - Miccai 2018, Pt Ii 8560_
ISBN of the book

978-3-030-00934-2

978-3-030-00933-5

Book part number

mateusz.kozinski@epfl.ch

Volume

11071

Start page

283

End page

291

Subjects

Computer Science, Theory & Methods

•

Computer Science 6531_

•

image segmentation

•

deep learning

•

detection of linear structures in microscopy images

•

neuron tracing in microscopy images

•

extraction of blood vessels in microscopy images

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CVLAB  
Event nameEvent placeEvent date
MICCAI

Granada, Spain

September 16-20, 2018

Available on Infoscience
September 12, 2018
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/148231
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