Synthesis for Dataset Augmentation of H&E Stained Images with Semantic Segmentation Masks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11120%2F23%3A43927134" target="_blank" >RIV/00216208:11120/23:43927134 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5220/0011679300003417" target="_blank" >https://doi.org/10.5220/0011679300003417</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0011679300003417" target="_blank" >10.5220/0011679300003417</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Synthesis for Dataset Augmentation of H&E Stained Images with Semantic Segmentation Masks

Popis výsledku v původním jazyce

The automatic analysis of medical images with the application of deep learning methods relies highly on the amount and quality of annotated data. Most of the diagnostic processes start with the segmentation and classification of cells. The manual annotation of a sufficient amount of high-variability data is extremely time-consuming, and the semi-automatic methods may introduce an error bias. Another research option is to use deep learning generative models to synthesize medical data with annotations as an extension to real datasets. Enhancing the training with synthetic data proved that it can improve the robustness and generalization of the models used in industrial problems. This paper presents a deep learning-based approach to generate synthetic histological stained images with corresponding multi-class annotated masks evaluated on cell semantic segmentation. We train conditional generative adversarial networks to synthesize a 6-channeled image. The six channels consist of the histological image and the annotations concerning the cell and organ type specified in the input. We evaluated the impact of the synthetic data on the training with the standard network UNet. We observe quantitative and qualitative changes in segmentation results from models trained on different distributions of real and synthetic data in the training batch.

Název v anglickém jazyce

Synthesis for Dataset Augmentation of H&E Stained Images with Semantic Segmentation Masks

Popis výsledku anglicky

The automatic analysis of medical images with the application of deep learning methods relies highly on the amount and quality of annotated data. Most of the diagnostic processes start with the segmentation and classification of cells. The manual annotation of a sufficient amount of high-variability data is extremely time-consuming, and the semi-automatic methods may introduce an error bias. Another research option is to use deep learning generative models to synthesize medical data with annotations as an extension to real datasets. Enhancing the training with synthetic data proved that it can improve the robustness and generalization of the models used in industrial problems. This paper presents a deep learning-based approach to generate synthetic histological stained images with corresponding multi-class annotated masks evaluated on cell semantic segmentation. We train conditional generative adversarial networks to synthesize a 6-channeled image. The six channels consist of the histological image and the annotations concerning the cell and organ type specified in the input. We evaluated the impact of the synthetic data on the training with the standard network UNet. We observe quantitative and qualitative changes in segmentation results from models trained on different distributions of real and synthetic data in the training batch.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP - Volume 4

ISBN

978-989-758-634-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

873-880

Název nakladatele

SciTePress

Místo vydání

Setúbal

Místo konání akce

Lisabon

Datum konání akce

19. 2. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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