Artificial Image Synthesis and Data Augmentation for Deep Learning Segmentation of Phase Contrast Images for Biomarker Discovery in Cancer Research
The result's identifiers
Result code in 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>
Result on the web
<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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Alternative languages
Result language
angličtina
Original language name
Artificial Image Synthesis and Data Augmentation for Deep Learning Segmentation of Phase Contrast Images for Biomarker Discovery in Cancer Research
Original language description
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
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů