We propose a loss function for training a Deep Neural Net- work (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 vas- cular 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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Learning to Segment 3D Linear Structures Using Only 2D Annotations
Dr. Mateusz Kozinski, EPFL
19 April 2018 · 11:33 a.m.