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

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.


Published in:
Medical Image Computing And Computer Assisted Intervention - Miccai 2018, Pt Ii 8560_, 11071, 283-291
Presented at:
MICCAI, Granada, Spain, September 16-20, 2018
Year:
Jan 01 2018
Publisher:
Cham, SPRINGER INTERNATIONAL PUBLISHING AG
ISSN:
0302-9743
1611-3349
ISBN:
978-3-030-00934-2
978-3-030-00933-5
Keywords:
Laboratories:




 Record created 2018-09-12, last modified 2019-12-10

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