2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020459" target="_blank" >RIV/62690094:18450/23:50020459 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10130458" target="_blank" >https://ieeexplore.ieee.org/document/10130458</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TIM.2023.3277975" target="_blank" >10.1109/TIM.2023.3277975</a>
Alternative languages
Result language
angličtina
Original language name
2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images
Original language description
Cancer cell segmentation is challenging since they grow in tightly packed colonies (clumps), causing adjacent cells to overlap. In this work, we proposed an automated vision-based analysis framework: a two-phase clump profiler (2pClPr) for the segmentation of cancer cells in fluorescence microscopy images. In the first phase, we proposed a deep learning (DL) network, Multiscale Cell-Net, for coarse segmentation. Another framework, multiscale region proposal network (MS-RPN), was simultaneously trained in parallel to Multiscale Cell-Net to generate seeds for each cell. The coarse segmentation map was unable to segment the complex clumps. We proposed a novel metric, the Irregularity factor (Iftr), to identify those complex clumps. Once identified, we mapped them with the seed points generated by MS-RPN. These seeds served as the initialization points for our proposed level-set framework: weighing repelling force embedded-level-set method (WRFe-LSM) which segments the identified complex clumps in the second phase of segmentation. The final segmentation map was generated with the segmented cells from the two phases. We conducted extensive experiments on our private dataset comprising images from four complex cancer cell lines and obtained an aggregated Jaccard index (AJI) of 76.6%, 72.9%, 75.5%, and 69.7% on HeLa, MDA-MB-468, MDA-MB-231, and T-47D, respectively. Furthermore, to show the generalization ability of 2pClPr, we conducted comparative experiments on a publicly available hematoxylin-eosin (H&E)-stained histopathological images dataset (MoNuSeg) and obtained an AJI of 66.2%. A detailed evaluation of segmentation performance on both the datasets shows that 2pClPr is robust and effective. © 1963-2012 IEEE.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Instrumentation and Measurement
ISSN
0018-9456
e-ISSN
1557-9662
Volume of the periodical
72
Issue of the periodical within the volume
May
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
"Article number: 5014914"
UT code for WoS article
001000758600003
EID of the result in the Scopus database
2-s2.0-85161010292