Title: MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Image
Authors: Jiřík, Miroslav
Moulisová, Vladimíra
Schindler, Claudia
Červenková, Lenka
Pálek, Richard
Rosendorf, Jáchym
Arlt, Janine
Bolek, Lukáš
Dejmek, Jiří
Dahmen, Uta
Jiříková, Kamila
Gruber, Ivan
Liška, Václav
Železný, Miloš
Citation: JIŘÍK, M. MOULISOVÁ, V. SCHINDLER, C. ČERVENKOVÁ, L. PÁLEK, R. ROSENDORF, J. ARLT, J. BOLEK, L. DEJMEK, J. DAHMEN, U. JIŘÍKOVÁ, K. GRUBER, I. LIŠKA, V. ŽELEZNÝ, M.MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Image. In: Combinatorial Image Analysis 20th International Workshop, IWCIA 2020, Novi Sad, Serbia, July 16–18, 2020, Proceedings. Cham: Springer, 2020. s. 209-218. ISBN 978-3-030-51001-5, ISSN 0302-9743.
Issue Date: 2020
Publisher: Springer
Document type: konferenční příspěvek
URI: 2-s2.0-85088565236
ISBN: 978-3-030-51001-5
ISSN: 0302-9743
Keywords in different language: microscopy;annotation;scaffold;liver;decellularization
Abstract in different language: Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations.
Rights: Plný text je přístupný v rámci univerzity přihlášeným uživatelům.
© Springer
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