Artificial Image Synthesis and Data Augmentation for Deep Learning Segmentation of Phase Contrast Images for Biomarker Discovery in Cancer Research

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU137084" target="_blank" >RIV/00216305:26220/19:PU137084 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.researchgate.net/publication/335363685_Artificial_Image_Synthesis_and_Data_Augmentation_for_Deep_Learning_Segmentation_of_Phase_Contrast_Images_for_Biomarker_Discovery_in_Cancer_Research" target="_blank" >https://www.researchgate.net/publication/335363685_Artificial_Image_Synthesis_and_Data_Augmentation_for_Deep_Learning_Segmentation_of_Phase_Contrast_Images_for_Biomarker_Discovery_in_Cancer_Research</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Image Synthesis and Data Augmentation for Deep Learning Segmentation of Phase Contrast Images for Biomarker Discovery in Cancer Research

Popis výsledku v původním jazyce

Deep learning (DL) algorithms are achieving or even surpassing human-level performance in tasks like image classification or segmentation. Due to their fast development and exceptional performance, DL algorithms were introduced in various life science domains such as biomedical imaging, bioinformatics or computational biology. However, the outcome of these algorithms on unseen data highly depends on the quality of the training dataset. Thus, there is a need for manual data annotation which is a lengthy and time-consuming process, especially in the field of cell imaging. We hereby propose a technique to accelerate data annotation by using synthetic phasecontrast images to train deep learning algorithms for cell segmentation. Uniformity of the statistical data distribution, specific image artefacts modelling or representability were defined, and the method was designed and implemented. The feasibility of the proposed method was demonstrated by training the Mask R-CNN model for instanceaware segmentation

Název v anglickém jazyce

Artificial Image Synthesis and Data Augmentation for Deep Learning Segmentation of Phase Contrast Images for Biomarker Discovery in Cancer Research

Popis výsledku anglicky

Deep learning (DL) algorithms are achieving or even surpassing human-level performance in tasks like image classification or segmentation. Due to their fast development and exceptional performance, DL algorithms were introduced in various life science domains such as biomedical imaging, bioinformatics or computational biology. However, the outcome of these algorithms on unseen data highly depends on the quality of the training dataset. Thus, there is a need for manual data annotation which is a lengthy and time-consuming process, especially in the field of cell imaging. We hereby propose a technique to accelerate data annotation by using synthetic phasecontrast images to train deep learning algorithms for cell segmentation. Uniformity of the statistical data distribution, specific image artefacts modelling or representability were defined, and the method was designed and implemented. The feasibility of the proposed method was demonstrated by training the Mask R-CNN model for instanceaware segmentation

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

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Návaznosti

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