Automatic Fusion of Segmentation and Tracking Labels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107241" target="_blank" >RIV/00216224:14330/19:00107241 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-11024-6_34" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-11024-6_34</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-11024-6_34" target="_blank" >10.1007/978-3-030-11024-6_34</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Fusion of Segmentation and Tracking Labels

Popis výsledku v původním jazyce

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 (https://github.com/xulman/CTC-FijiPlugins).

Název v anglickém jazyce

Automatic Fusion of Segmentation and Tracking Labels

Popis výsledku anglicky

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 (https://github.com/xulman/CTC-FijiPlugins).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP302%2F12%2FG157" target="_blank" >GBP302/12/G157: Dynamika a organizace chromosomů během buněčného cyklu a při diferenciaci v normě a patologii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer Vision – ECCV 2018 Workshops

ISBN

9783030110239

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

446-454

Název nakladatele

Springer Nature

Místo vydání

Switzerland

Místo konání akce

Munich, Germany

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000594200000034