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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GA17-05048S" target="_blank" >GA17-05048S: Segmentace a trekování živých buněk v multimodálních obrazech</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE 16th International Symposium on Biomedical Imaging

ISBN

9781538636411

ISSN

1945-7928

e-ISSN

1945-8452

Počet stran výsledku

4

Strana od-do

236-239

Název nakladatele

IEEE 16th International Symposium on Biomedical Imaging

Místo vydání

Venice, Italy, Italy

Místo konání akce

Venice, Italy, Italy

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000485040000055