Mr. Hong He Profile

Hong He

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 March 2019 Paper

Automatic cell segmentation using mini-u-net on fluorescence in situ hybridization images

Jianhuo Shen, Teng Li, Chuanrui Hu, Hong He, Jianfei Liu

Proceedings Volume 10950, 109502T (2019) https://doi.org/10.1117/12.2513394

KEYWORDS: Image segmentation, Convolution, Luminescence, Image analysis, Image processing, Biotechnology, Image processing algorithms and systems, Neural networks

Read Abstract +

Fluorescent in situ hybridization (FISH) is a molecular cytogenetic technique that provides reliable imaging biomarkers to diagnose cancer and genetic disorders in the cellular level. One prerequisite step to identify carcinoma cells in FISH images is to accurately segment cells, so as to quantify DNA/RNA signals within each cell. Manual cell segmentation is a tedious and time-consuming task, which demands automatic methods. However, automatic cell segmentation is hindered by low image contrast, weak cell boundaries, and cell touching in FISH images. In this paper, we develop a fast mini-U-Net method to address these challenges. Some special characteristics are tailored in the mini-U-Net, including connections between input images and their feature maps to accurately localize cells, mlpcon (multilayer perceptron + convolution) to segment cell regions, and morphology operators and the watershed algorithm to separate each individual cell. In comparison with the U-Net, the miniU-Net has fewer training parameters and less computational cost. The validation on 510 cells indicated that the Dice coefficients of the mini-U-Net and U-Net were 80.20% and 77.27%, and area overlap ratios were 69.17% and 68.04%, respectively. These promising results suggest that the mini-U-Net could generate accurate cell segmentation for fully automatic FISH image analysis.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years