Original title: Augmentation Technique For Artificial Phase-Contrast Microscopy Images Generation For The Training Of Deep Learning Algorithms
Authors: Mívalt, Filip
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Phase contrast segmentation is crucial for various biological tasks such us quantitative, comparative or single cell level analysis. The popularity of image segmentation using deep learning strategies has been transferred into the field of microscopy imaging as well. Since the huge amount of training data is usually required, the annotation is time-consuming and lengthy. This paper introduces the method and augmentation techniques for artificial phase-contrast images generation aiming at the training of deep learning algorithms.
Keywords: artificial data generation; cell segmentation; data augmentation; deep learning; phase-contrast
Host item entry: Proceedings of the 25st Conference STUDENT EEICT 2019, ISBN 978-80-214-5735-5

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/186652

Permalink: http://www.nusl.cz/ntk/nusl-414554


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Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers