Convolution-based Soma Counting Algorithm for Confocal Microscopy Image Stacks

Shih-Ting Huang, Yue Jiang, Hao-Chiang Shao

2021

Abstract

To facilitate brain research, scientists need to identify factors that can promote or suppress neural cell differentiation mechanisms. Accordingly, the way to recognize, segment, and count developing neural cells within a microscope image stack becomes a fundamental yet considerable issue. However, it is currently not feasible to develop a DCNN (deep convolutional neural network) based segmentation algorithm for confocal fluorescence image stacks because of the lack of manual-annotated segmentation ground truth. Also, such tasks traditionally require meticulous manual preprocessing steps, and such manual steps make the results unstable even with software support like ImageJ. To solve this problem, we propose in this paper a convolution-based algorithm for cell recognizing and counting. The proposed method is computationally efficient and nearly parameter-free. For a 1024×1024×70 two-channel image volume containing about 100 developing neuron cells, our method can finish the recognition and counting tasks within 250 seconds with a standard deviation smaller than 4 comparing with manual cell-counting results

Paper Citation

in Harvard Style

Huang S., Jiang Y. and Shao H. (2021). Convolution-based Soma Counting Algorithm for Confocal Microscopy Image Stacks. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS; ISBN 978-989-758-490-9, SciTePress, pages 351-356. DOI: 10.5220/0010388400002865

in Bibtex Style

@conference{biosignals21,
author={Shih-Ting Huang and Yue Jiang and Hao-Chiang Shao},
title={Convolution-based Soma Counting Algorithm for Confocal Microscopy Image Stacks},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS},
year={2021},
pages={351-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010388400002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS
TI - Convolution-based Soma Counting Algorithm for Confocal Microscopy Image Stacks
SN - 978-989-758-490-9
AU - Huang S.
AU - Jiang Y.
AU - Shao H.
PY - 2021
SP - 351
EP - 356
DO - 10.5220/0010388400002865
PB - SciTePress