Deep Learning Pipeline for Chromosome Segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU145357" target="_blank" >RIV/00216305:26220/22:PU145357 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9764950" target="_blank" >https://ieeexplore.ieee.org/document/9764950</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA54537.2022.9764950" target="_blank" >10.1109/RADIOELEKTRONIKA54537.2022.9764950</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning Pipeline for Chromosome Segmentation
Original language description
Chromosome segmentation is a challenging and time-consuming part of karyotyping and requires a high level of expertise. Computer segmentation algorithms still require the assistance of cytologists in more complicated cases with overlapping or touching chromosomes. Deep learning models have the potential to make the segmentation process completely automated, and their applications are currently actively re-searched. This paper proposes a segmentation pipeline by using deep learning models and traditional computer vision algorithms. This process can be split into four steps, in which we use U-Net architecture to remove any background noises of the metaphase image. Next, we use thresholding and skeletonization to extract and classify single chromosomes and chromosome clusters. As a final step, we use Mask R-CNN, for instance, segmentation on the overlapping and touching chromosomes, and apply test-time augmentation to improve the model's precision.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA)
ISBN
978-1-7281-8686-3
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
197-201
Publisher name
IEEE
Place of publication
neuveden
Event location
Košice
Event date
Apr 21, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000856002200041