Automated mitosis detection of stem cell populations in phase-contrast microscopy images

Seungil Huh, Dai Fei Elmer Ker, Ryoma Bise, Mei Chen, Takeo Kanade

Research output: Contribution to journalArticle

76 Citations (Scopus)

Abstract

Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.

Original languageEnglish
Article number5606202
Pages (from-to)586-596
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume30
Issue number3
DOIs
Publication statusPublished - Mar 1 2011
Externally publishedYes

Fingerprint

Phase-Contrast Microscopy
Stem cells
Mitosis
Microscopic examination
Stem Cells
Cells
Cell Division
Population
Cell proliferation
Computational efficiency
Regenerative Medicine
Statistical Models
Throughput
Imaging techniques
Microscopy
Cell Culture Techniques
Monitoring
Cell Proliferation
Parturition
Staining and Labeling

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Automated mitosis detection of stem cell populations in phase-contrast microscopy images. / Huh, Seungil; Ker, Dai Fei Elmer; Bise, Ryoma; Chen, Mei; Kanade, Takeo.

In: IEEE Transactions on Medical Imaging, Vol. 30, No. 3, 5606202, 01.03.2011, p. 586-596.

Research output: Contribution to journalArticle

Huh, Seungil ; Ker, Dai Fei Elmer ; Bise, Ryoma ; Chen, Mei ; Kanade, Takeo. / Automated mitosis detection of stem cell populations in phase-contrast microscopy images. In: IEEE Transactions on Medical Imaging. 2011 ; Vol. 30, No. 3. pp. 586-596.
@article{d90d3798d2e54691be610476919e37bd,
title = "Automated mitosis detection of stem cell populations in phase-contrast microscopy images",
abstract = "Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.",
author = "Seungil Huh and Ker, {Dai Fei Elmer} and Ryoma Bise and Mei Chen and Takeo Kanade",
year = "2011",
month = "3",
day = "1",
doi = "10.1109/TMI.2010.2089384",
language = "English",
volume = "30",
pages = "586--596",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Automated mitosis detection of stem cell populations in phase-contrast microscopy images

AU - Huh, Seungil

AU - Ker, Dai Fei Elmer

AU - Bise, Ryoma

AU - Chen, Mei

AU - Kanade, Takeo

PY - 2011/3/1

Y1 - 2011/3/1

N2 - Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.

AB - Due to the enormous potential and impact that stem cells may have on regenerative medicine, there has been a rapidly growing interest for tools to analyze and characterize the behaviors of these cells in vitro in an automated and high throughput fashion. Among these behaviors, mitosis, or cell division, is important since stem cells proliferate and renew themselves through mitosis. However, current automated systems for measuring cell proliferation often require destructive or sacrificial methods of cell manipulation such as cell lysis or in vitro staining. In this paper, we propose an effective approach for automated mitosis detection using phase-contrast time-lapse microscopy, which is a nondestructive imaging modality, thereby allowing continuous monitoring of cells in culture. In our approach, we present a probabilistic model for event detection, which can simultaneously 1) identify spatio-temporal patch sequences that contain a mitotic event and 2) localize a birth event, defined as the time and location at which cell division is completed and two daughter cells are born. Our approach significantly outperforms previous approaches in terms of both detection accuracy and computational efficiency, when applied to multipotent C3H10T1/2 mesenchymal and C2C12 myoblastic stem cell populations.

UR - http://www.scopus.com/inward/record.url?scp=79952155703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952155703&partnerID=8YFLogxK

U2 - 10.1109/TMI.2010.2089384

DO - 10.1109/TMI.2010.2089384

M3 - Article

C2 - 21356609

AN - SCOPUS:79952155703

VL - 30

SP - 586

EP - 596

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 3

M1 - 5606202

ER -