Prof. Sungbin Lim Profile

Prof. Sungbin Lim

at Ulsan National Institute of Science and Technology

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 | 5 March 2021 Presentation + Paper

3D cell instance segmentation via point proposals using cellular components

Jinho Choi, Junwoo Park, Hyeon-seok Min, Hyungjoo Cho, Sungbin Lim, Jaegul Choo

Proceedings Volume 11647, 116471G (2021) https://doi.org/10.1117/12.2581796

KEYWORDS: Image segmentation, 3D modeling, 3D image processing, Stereoscopy, Quantitative analysis, Microscopy, Data modeling

Read Abstract +

Cell instance segmentation is a critical task to perform for the quantitative analysis of 3D live-cell images. Existing studies mostly apply a region proposal-based approach to instance segmentation of microscopy images. However, they often fail to detect cells in 3D live-cell images, which have complicated and heterogeneous shapes, often closely linked to the neighborhood cells. A different approach based on point proposal methods is more robust in handling complex shapes than the box proposal. These methods take an image and a proposed point in the form of its location (x; y) as input and generate a mask for an object that includes the point. They also show that the model can improve the prediction by utilizing negative point proposals chosen from false-positive areas. In this paper, we propose a novel cell instance segmentation approach based on point proposal for 3D cell imaging. Different from existing work, however, our model utilizes the nuclei of cells as point proposal and employ them as positive and negative point proposals. We constructed the 3D NIH3T3 dataset for training and evaluation, and examine the proposed model qualitatively on three independently gathered cells; HeLa, A549, and MDA-MB-231. Our model exhibits superior quantitative results; moreover, compared to previous methods, it properly predicts cell lines, which are not even well-annotated during training.

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