Managing class imbalance and differential staining of immune cell populations in multi-class instance segmentation of multiplexed immunofluorescence images of Lupus Nephritis biopsies

Madeleine S. Durkee; Rebecca Abraham; Junting Ai; Marcus R. Clark; Maryellen L. Giger

doi:10.1117/12.2581060

15 February 2021 Managing class imbalance and differential staining of immune cell populations in multi-class instance segmentation of multiplexed immunofluorescence images of Lupus Nephritis biopsies

Madeleine S. Durkee, Rebecca Abraham, Junting Ai, Marcus R. Clark, Maryellen L. Giger

Author Affiliations +

Proceedings Volume 11603, Medical Imaging 2021: Digital Pathology; 1160313 (2021) https://doi.org/10.1117/12.2581060
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Lupus nephritis (LuN) is a manifestation of systemic lupus erythematosus defined by chronic infiltration of immune cells into the kidneys—particularly lymphocytes and dendritic cells (DCs). Ultimately, our goal is to characterize the cellular communities associated with progression to kidney failure. To accomplish this, we have generated a dataset of fluorescence confocal microscopy images of kidney biopsies from 31 LuN patients that have been stained for two T-lymphocyte populations, B-lymphocytes and two DC populations. We are using convolutional neural networks (CNNs) with a Mask R-CNN architecture to perform instance segmentation on these five classes. This multi-class instance segmentation task is hindered by an inherent class imbalance between lymphocytes and DCs, with DCs being much less prevalent. Here we discuss methods for managing class imbalance to achieve comparable instance segmentation of both DCs and lymphocytes in LuN biopsies. A network trained to identify all 5 classes yielded higher sensitivity to DCs when the training set was filtered to contain images with all 5 cell classes present. Average DC sensitivity on an independent test set improved from 0.54 to 0.63 with filtered training data. DC segmentation improved further when the network was trained specifically for DC classes. Average DC sensitivity reached 0.91 when trained separately from lymphocytes, with average Jaccard index of DCs improving from 0.69±0.2 to 0.76±0.2. Accurate segmentation of all cell types relevant to LuN pathogenesis enabled in-depth spatial analysis of the immune environments that result in renal failure in LuN patients.

Citation Download Citation

Madeleine S. Durkee, Rebecca Abraham, Junting Ai, Marcus R. Clark, and Maryellen L. Giger "Managing class imbalance and differential staining of immune cell populations in multi-class instance segmentation of multiplexed immunofluorescence images of Lupus Nephritis biopsies", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 1160313 (15 February 2021); https://doi.org/10.1117/12.2581060

ACCESS THE FULL ARTICLE

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