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@inproceedings{1488155,
author = {Lux, Filip and Matula, Petr},
address = {Venice, Italy, Italy},
booktitle = {IEEE 16th International Symposium on Biomedical Imaging},
doi = {http://dx.doi.org/10.1109/ISBI.2019.8759594},
keywords = {Image Segmentation; Differential Interface Contrast; Convolutional Neural Networks; Watershed},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Venice, Italy, Italy},
isbn = {978-1-5386-3641-1},
pages = {236-239},
publisher = {IEEE 16th International Symposium on Biomedical Imaging},
title = {DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed},
url = {https://ieeexplore.ieee.org/document/8759594},
year = {2019}
}
TY - JOUR
ID - 1488155
AU - Lux, Filip - Matula, Petr
PY - 2019
TI - DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed
PB - IEEE 16th International Symposium on Biomedical Imaging
CY - Venice, Italy, Italy
SN - 9781538636411
KW - Image Segmentation
KW - Differential Interface Contrast
KW - Convolutional Neural Networks
KW - Watershed
UR - https://ieeexplore.ieee.org/document/8759594
N2 - Image segmentation of dense cell populations acquired using label-free optical microscopy techniques is a challenging problem. In this paper, we propose a novel approach based on a combination of deep learning and watershed transform to segment differential interference contrast (DIC) images with high accuracy. The main idea of our approach is to train a convolutional neural network to detect both cellular markers and cellular areas and based on these predictions to split the individual cells by using the watershed transform. The approach was developed based on the images of dense HeLa cell populations included in the Cell Tracking Challenge database. Our approach was ranked the best in segmentation, detection, as well as the overall performance as evaluated on the challenge datasets.
ER -
LUX, Filip a Petr MATULA. DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed. Online. In \textit{IEEE 16th International Symposium on Biomedical Imaging}. Venice, Italy, Italy: IEEE 16th International Symposium on Biomedical Imaging, 2019, s.~236-239. ISBN~978-1-5386-3641-1. Dostupné z: https://dx.doi.org/10.1109/ISBI.2019.8759594.
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