Publication detail

Augmentation Technique for Artificial Phase-Contrast Microscopy Image Synthesis for the Training of Deep Learning Algorithms

MÍVALT, F.

Original Title

Augmentation Technique for Artificial Phase-Contrast Microscopy Image Synthesis for the Training of Deep Learning Algorithms

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Phase contrast image segmentation is crucial for various biological tasks such as quantitative or comparative analysis at single cell level. Deep learning-based image segmentation has been transferred into the field of microscopy imaging. A large amount of precisely annotated cells is required. Thus, the annotation process is for the experts lengthy and time-consuming. This paper introduces a strategy and augmentation technique for artificial phase-contrast images synthesis aiming to train and support the generalisation ability of deep learning algorithms.

Keywords

deep learning, phase-contrast, cell segmentation, data augmentation, artificial data gen- eration

Authors

MÍVALT, F.

Released

25. 4. 2019

Location

Brno

ISBN

978-80-214-5735

Book

Proceedings of the 25th Conference STUDENT EEICT 2019

Edition number

1

Pages from

199

Pages to

202

Pages count

2

URL

https://www.researchgate.net/publication/335365184_Augmentation_Technique_for_Artificial_Phase-Contrast_Microscopy_Image_Synthesis_for_the_Training_of_Deep_Learning_Algorithms

BibTex

@inproceedings{BUT165371,
  author="Filip {Mívalt}",
  title="Augmentation Technique for Artificial Phase-Contrast Microscopy Image Synthesis for the Training of Deep Learning Algorithms",
  booktitle="Proceedings of the 25th Conference STUDENT EEICT 2019",
  year="2019",
  number="1",
  pages="199--202",
  address="Brno",
  doi="10.13140/RG.2.2.32827.16160",
  isbn="978-80-214-5735",
  url="https://www.researchgate.net/publication/335365184_Augmentation_Technique_for_Artificial_Phase-Contrast_Microscopy_Image_Synthesis_for_the_Training_of_Deep_Learning_Algorithms"
}