2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images

Popis výsledku v původním jazyce

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&amp;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.

Název v anglickém jazyce

2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images

Popis výsledku anglicky

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&amp;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Instrumentation and Measurement

ISSN

0018-9456

e-ISSN

1557-9662

Svazek periodika

72

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

"Article number: 5014914"

Kód UT WoS článku

001000758600003

EID výsledku v databázi Scopus

2-s2.0-85161010292