Mr. Jan-Niklas Schneider Profile

Jan-Niklas Schneider

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 | 2 April 2024 Presentation + Paper

Unsupervised cell segmentation by invariant information clustering

Wessel van Nierop, Jan-N. Schneider, Peter H. de With, Fons van der Sommen

Proceedings Volume 12926, 129260V (2024) https://doi.org/10.1117/12.3001442

KEYWORDS: Live cell imaging, Machine learning, Deep learning, Information theory, Image segmentation, Data modeling, Performance modeling, Tunable filters, Ablation, Microscopy, Semantics, Transform theory, Image filtering

Read Abstract +

Cell segmentation is a crucial task in brightfield microscopy for cellular analysis. Conventional segmentation methods, including thresholding and supervised deep learning approaches, have limitations due to weak cell boundaries and the requirement for expert annotations of cells. To address these challenges, this work proposes an unsupervised cell-segmentation model. Specifically, the model maximizes mutual information between an artificially created data pair to train a neural network for cell segmentation. The proposed method achieves the fastest inference time and requires only a small fraction of network parameters compared to reported state-of-the-art alternatives from literature, making the proposal more efficient for live-cell experiments where large amounts of data are collected. A quantitative comparison with other state-of-the-art models shows that the unsupervised model outperforms relevant supervised models in terms of the Jaccard index. In detail, the unsupervised model achieves a remarkable Jaccard score of 0.71 on a out-of-sample proprietary dataset which is fully annotated. This represents a notable improvement of 4.0% compared to the supervised Cellpose model and an impressive 11.4% increase compared to the unsupervised Leopart model. In conclusion, the proposed model effectively segments cells of six types without the need for expert annotations, thereby making it a valuable tool for cellular analysis. The source code is available at https://github.com/wesselvannierop/wescell.

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