Automatic Fusion of Segmentation and Tracking Labels.

First Authors Cem Emre Akbas
Authors Cem Emre Akbas, Vladimir Ulman, Martin Maska, Florian Jug, Michal Kozubek
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Last Authors Michal Kozubek
Conference Proceedings Volume Title Computer Vision – ECCV 2018 Workshops : Munich, Germany, September 8-14, 2018, Proceedings, Part VI
Series Title Lecture Notes in Computer Science ; 11134
Conference Name Computer Vision – ECCV 2018 Workshops
Conference Location Munich
Conference Start Date 2018-09-08
Conference End Date 2018-09-14
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Publisher Springer International Publishing
Conference Proceedings Editors Laura Leal-Taixé
ISBN 978-3-030-11024-6
First Page 446
Last Page 454
Print Publication Date 2019-01-23
Online Publication Date 2019-01-23
Abstract Labeled training images of high quality are required for developing well-working analysis pipelines. This is, of course, also true for biological image data, where such labels are usually hard to get. We distinguish human labels (gold corpora) and labels generated by computer algorithms (silver corpora). A naturally arising problem is to merge multiple corpora into larger bodies of labeled training datasets. While fusion of labels in static images is already an established field, dealing with labels in time-lapse image data remains to be explored. Obtaining a gold corpus for segmentation is usually very time-consuming and hence expensive. For this reason, gold corpora for object tracking often use object detection markers instead of dense segmentations. If dense segmentations of tracked objects are desired later on, an automatic merge of the detection-based gold corpus with (silver) corpora of the individual time points for segmentation will be necessary. Here we present such an automatic merging system and demonstrate its utility on corpora from the Cell Tracking Challenge. We additionally release all label fusion algorithms as freely available and open plugins for Fiji (
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DOI doi:10.1007/978-3-030-11024-6_34
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Created By thuem
Added Date 2019-02-01
Last Edited By thuem
Last Edited Date 2020-05-19 14:22:32.095
Library ID 7320
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