Deep-learning-based fully automatic spine centerline detection in CT data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU132793" target="_blank" >RIV/00216305:26220/19:PU132793 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8856528" target="_blank" >https://ieeexplore.ieee.org/document/8856528</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EMBC.2019.8856528" target="_blank" >10.1109/EMBC.2019.8856528</a>
Alternative languages
Result language
angličtina
Original language name
Deep-learning-based fully automatic spine centerline detection in CT data
Original language description
In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20601 - Medical engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
ISBN
978-1-5386-1312-2
ISSN
1557-170X
e-ISSN
—
Number of pages
4
Pages from-to
2407-2410
Publisher name
IEEE
Place of publication
Berlin, Germany
Event location
Berlin
Event date
Jul 23, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000557295302190