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Proceedings Paper

Improved instance segmentation of immune cells in human lupus nephritis biopsies with Mask R-CNN
Author(s): Madeleine S. Durkee; Adam Sibley; Junting Ai; Rebecca Abraham; Vladimir M. Liarski; Marcus R. Clark; Maryellen L. Giger
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Paper Abstract

We demonstrate an instance segmentation method with Mask R-CNN using a ResNet-101 plus Feature Pyramid Network convolutional backbone to segment and classify T cells and antigen presenting cells (APCs) in multi-channel fluorescence confocal images. This network improves on our previous cell distance mapping (CDM) pipeline, which used a custom 10- layer convolutional neural network for cell segmentation. We have validated Mask R-CNN on two independent datasets of fluorescence confocal images: 1) mouse lymph node tissue, and 2) human lupus nephritis (LuN) biopsies. For dataset 1, mice were injected with fluorescent dendritic cells and two populations of fluorescent T cells. Mask R-CNN improved sensitivity averaged across all cell types from 0.88 to 0.94. Specificity improved from 0.92 to 0.95 across all cell types, and intersection over union score (IOU) improved significantly from 0.82 to 0.86 (p < 0.0001). Human LuN biopsies in dataset 2 were stained with two T cell markers and two APC markers, with separate staining panels to identify different populations of APCs. Mask R-CNN again improved segmentation and classification averaged across all cell types, increasing overall sensitivity from 0.72 to 0.76, specificity from 0.86 to 0.93, and significantly increasing IOU from 0.71 to 0.81 (p < 0.0001). Improved IOU scores are particularly important in CDM to be able to quantify cell shape for identification of functional interactions of immune cells. Mask R-CNN is therefore a superior method for instance segmentation of immune cells in microscopy images for image analysis of cellular function in pathological immune states.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132019 (16 March 2020); doi: 10.1117/12.2549751
Show Author Affiliations
Madeleine S. Durkee, The Univ. of Chicago (United States)
Adam Sibley, The Univ. of Chicago (United States)
Junting Ai, The Univ. of Chicago (United States)
Rebecca Abraham, The Univ. of Chicago (United States)
Vladimir M. Liarski, The Univ. of Chicago (United States)
Marcus R. Clark, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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