Mr. Benjamin A. Cifu Profile

Benjamin A. Cifu

at Univ of Chicago

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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

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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.

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