Mr. Benjamin A. Cifu Profile

Benjamin A. Cifu

at Univ of Chicago

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 | 17 February 2020 Presentation + Paper

Instance segmentation of immune cells in human lupus nephritis using deep learning: comparing performance on sample preparation and staining panels

Madeleine Durkee, Rebecca Abraham, Adam Sibley, Benjamin Cifu, Junting Ai, Vladimir Liarski, Marcus Clark, Maryellen Giger

Proceedings Volume 11243, 112430T (2020) https://doi.org/10.1117/12.2545110

KEYWORDS: Image segmentation, Tissues, Biopsy, Confocal microscopy, Network architectures, Computer vision technology, Image analysis

Read Abstract +

Computer vision and deep learning are integral tools in the improvement of high-throughput analysis of cellular images. Specifically, optimization of algorithms for object detection and instance segmentation tasks are important in cellular image analysis to segment and classify multi-object, multi-class images. In this work, we employ an instance segmentation pipeline with Mask RCNN, using a ResNet-101 and Feature Pyramid Network convolutional backbone to segment and classify T cells and antigen presenting cells (APCs) in multi-channel fluorescence confocal images of lupus nephritis biopsies. This task was first performed on a dataset of fresh frozen biopsies stained for T cells (CD3 and CD4) and two APC populations: 1) myeloid dendritic cells (BDCA1 and CD11c), and 2) plasmacytoid dendritic cells (BDCA2 and CD123). The network achieved an average sensitivity of 0.82, specificity of 0.91, and Jaccard index of 0.79 across all cell types. However, relative to fresh frozen tissue, samples prepared through formalin fixation and paraffin embedding (FFPE) provide larger potential datasets for investigating immune activity. Training this same network architecture on an FFPE database of lupus nephritis tissue stained with the same antibody panel, the network achieved an average sensitivity of 0.82, specificity of 0.92, and Jaccard index of 0.77 across all cell types. In addition to working with FFPE tissue, it would also be beneficial to identify APCs with a single stain and image more cell types with a single staining panel. We have trained this network on a single-stained APC panel FFPE dataset to achieve an average sensitivity of 0.79, specificity of 0.86, and Jaccard index of 0.63 across all cell types. These three trained networks were used to assess differences in cell shape features between fixation and staining protocols.

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