DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107237" target="_blank" >RIV/00216224:14330/19:00107237 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ISBI.2019.8759594" target="_blank" >http://dx.doi.org/10.1109/ISBI.2019.8759594</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISBI.2019.8759594" target="_blank" >10.1109/ISBI.2019.8759594</a>

Result language
angličtina
Original language name
DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA17-05048S" target="_blank" >GA17-05048S: Multi-modal live cell image segmentation and tracking</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
IEEE 16th International Symposium on Biomedical Imaging
ISBN
9781538636411
ISSN
1945-7928
e-ISSN
1945-8452
Number of pages
4
Pages from-to
236-239
Publisher name
IEEE 16th International Symposium on Biomedical Imaging
Place of publication
Venice, Italy, Italy
Event location
Venice, Italy, Italy
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000485040000055

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