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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

30224 - Radiology, nuclear medicine and medical imaging

Project

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Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2023

Confidentiality

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

Article name in the collection

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

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e-ISSN

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Number of pages

8

Pages from-to

873-880

Publisher name

SciTePress

Place of publication

Setúbal

Event location

Lisabon

Event date

Feb 19, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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