Dr. Dina Bassiouny Profile

Dr. Dina Bassiouny

at Sunnybrook Health Sciences Ctr

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 | 7 April 2023 Presentation + Paper

Ink removal in whole slide images using hallucinated data

Vishwesh Ramanathan, Wenchao Han, Dina Bassiouny, Eileen Rakovitch, Anne Martel

Proceedings Volume 12471, 124710Y (2023) https://doi.org/10.1117/12.2653281

KEYWORDS: Tissues, Histopathology, Color, Pathology, Deep learning, Quality control, Image classification

Read Abstract +

Pathologists regularly use ink markings on histopathology slides to highlight specific areas of interest or orientation, making it an integral part of the workflow. Unfortunately, digitization of these ink-annotated slides hinders any computer-aided analyses, particularly deep learning algorithms, which require clean data free from artifacts. We propose a methodology that can identify and remove the ink markings for the purpose of computational analyses. We propose a two-stage network with a binary classifier for ink filtering and Pix2Pix for ink removal. We trained our network by artificially generating pseudo ink markings using only clean slides, requiring no manual annotation or curation of data. Furthermore, we demonstrate our algorithm’s efficacy over an independent dataset of H&E stained breast carcinoma slides scanned before and after the removal of pen markings. Our quantitative analysis shows promising results, achieving 98.7% accuracy for the binary classifier. For Pix2Pix, we observed a 65.6% increase in structure similarity index, a 21.3% increase in peak signal-to-noise ratio, and a 30% increase in visual information fidelity. As only clean slides are required for training, the pipeline can be adapted to multiple colors of ink markings or new domains, making it easy to deploy over different sets of histopathology slides. Code and trained models are available at: https://github.com/Vishwesh4/Ink-WSI.

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