U-Net in Histological Segmentation: Comparison of the Effect of Using Different Color Spaces and Final Activation Functions

László Körmöczi, László Nyúl

2025

Abstract

Deep neural networks became widespread in numerous fields of image processing, including semantic segmentation. U-Net is a popular choice for semantic segmentation of microscopy images, e.g. histological sections. In this paper, we compare the performance of a U-Net architecture in three different color spaces: the commonly used, perceptually uniform sRGB, the perceptually uniform but device-independent CIE L*a*b*, and linear RGB color space that is uniform in terms of light intensity. Furthermore, we investigate the network’s performance on data combinations that were unseen during training.

Paper Citation

in Harvard Style

Körmöczi L. and Nyúl L. (2025). U-Net in Histological Segmentation: Comparison of the Effect of Using Different Color Spaces and Final Activation Functions. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 386-389. DOI: 10.5220/0013323300003911

in Bibtex Style

@conference{bioimaging25,
author={László Körmöczi and László Nyúl},
title={U-Net in Histological Segmentation: Comparison of the Effect of Using Different Color Spaces and Final Activation Functions},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={386-389},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013323300003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - U-Net in Histological Segmentation: Comparison of the Effect of Using Different Color Spaces and Final Activation Functions
SN - 978-989-758-731-3
AU - Körmöczi L.
AU - Nyúl L.
PY - 2025
SP - 386
EP - 389
DO - 10.5220/0013323300003911
PB - SciTePress