Prof. Marcus R. Clark Profile

Prof. Marcus R. Clark

Professor of Medicine and Pathology at Univ of Chicago Medicine

SPIE Involvement:

Author

Publications (21)

SPIE Journal Paper | 10 December 2024 Open Access

Pseudo-spectral angle mapping for pixel and cell classification in highly multiplexed immunofluorescence images

Madeleine Torcasso, Junting Ai, Gabriel Casella, Thao Cao, Anthony Chang, Ariel Halper-Stromberg, Bana Jabri, Marcus Clark, Maryellen Giger

JMI, Vol. 11, Issue 06, 067502, (December 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.067502

KEYWORDS: Image classification, Image segmentation, Kidney, Biological imaging, Biological samples, Multiplexing, Tissues, Decision trees, Biopsy, Data modeling

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Proceedings Article | 29 May 2024 Paper

Quantitative analysis of high-plex immunofluorescence microscopy images to investigate the breast cancer tumor microenvironment

Madeleine Torcasso, Frederick Howard, Yuanyuan Zha, Junting Ai, Marcus Clark, Maryellen Giger

Proceedings Volume 13174, 1317413 (2024) https://doi.org/10.1117/12.3027025

KEYWORDS: Breast cancer, Microscopy, Quantitative analysis, Multiplexing, Fluorescence microscopy, Cancer, Tumors, Image segmentation, Biopsy

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Proceedings Article | 3 April 2024 Paper

Pixel-level analysis of protein markers in highly multiplexed immunofluorescence images of kidney biopsies

Madeleine Durkee, Junting Ai, Gabriel Casella, Thao Cao, Michael Andrade, Marcus Clark, Maryellen Giger

Proceedings Volume 12933, 1293313 (2024) https://doi.org/10.1117/12.3008522

KEYWORDS: Biopsy, Kidney, Multiplexing, Tissues, Biological research, Pathology, Rheumatology, Diseases and disorders

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Proceedings Article | 3 April 2024 Poster + Paper

Renal cell classification in highly multiplexed microscopy imaging using a biology-based decision tree classifier

Gabriel Casella, Madeleine Durkee, Junting Ai, Thao Cao, Deepjyoti Ghosh, Michael Andrade, Anthony Chang, Maryellen Giger, Marcus Clark

Proceedings Volume 12933, 129331H (2024) https://doi.org/10.1117/12.3008519

KEYWORDS: Decision trees, Biopsy, Biological imaging, Diseases and disorders, Tissues, Image segmentation, Multiplexing, Microscopy, Kidney

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Proceedings Article | 3 April 2024 Poster + Paper

Multi-class instance segmentation of renal structures in highly multiplexed immunofluorescence microscopy images

Thao Cao, Madeleine Durkee, Junting Ai, Gabriel Casella, Deepjyoti Ghosh, Anthony Chang, Michael Andrade, Maryellen Giger, Marcus Clark

Proceedings Volume 12933, 129331I (2024) https://doi.org/10.1117/12.3008544

KEYWORDS: Image segmentation, Kidney, Education and training, Multiplexing, Biopsy, Tissues, Inflammation, Fluorescence, Microscopy, Machine learning

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Proceedings Article | 12 March 2024 Presentation + Paper

Pseudo-spectral angle mapping to improve immune cell classification in highly multiplexed fluorescence microscopy images

Madeleine Durkee, Junting Ai, Gabriel Casella, Thao Cao, Marcus Clark, Maryellen Giger

Proceedings Volume 12846, 1284603 (2024) https://doi.org/10.1117/12.3003486

KEYWORDS: Artificial intelligence, Machine learning, Image segmentation, Image classification, Decision trees, Tissues, Biopsy, Multiplexing, Fluorescence microscopy, Colon, Biological imaging

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SPIE Journal Paper | 14 December 2023 Open Access

Generalizations of the Jaccard index and Sørensen index for assessing agreement across multiple readers in object detection and instance segmentation in biomedical imaging

Madeleine Durkee, Kyle Lleras, Karen Drukker, Junting Ai, Thao Cao, Gabriel Casella, Deepjyoti Ghosh, Marcus Clark, Maryellen Giger

JMI, Vol. 10, Issue 06, 065503, (December 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.065503

KEYWORDS: Image segmentation, Object detection, Matrices, Education and training, Detection and tracking algorithms, Deep convolutional neural networks, Biomedical optics, Machine learning, Image processing algorithms and systems, Biopsy

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Proceedings Article | 3 April 2023 Presentation + Paper

Convolutional neural networks detect cells in densely packed images at performance levels similar to human readers

Madeleine Durkee, Nevaeh Petrie, Kyle Lleras, Junting Ai, Rebecca Abraham, J. Cy Chittenden, Chasity Kasir, Fiona Clark, Gabriel Casella, Marcus Clark, Maryellen Giger

Proceedings Volume 12467, 124670J (2023) https://doi.org/10.1117/12.2654521

KEYWORDS: Image segmentation, Fluorescence, Biopsy, Image resolution, Machine learning, Multiplexing, Convolutional neural networks, Tissues, Pathology

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Proceedings Article | 15 March 2023 Presentation

Automatic detection of tertiary lymphoid structures in whole-slide images of triple-negative breast cancer biopsies (Conference Presentation)

Madeleine Durkee, Rebecca Abraham, Marcus Clark, Maryellen Giger

Proceedings Volume PC12383, PC123830A (2023) https://doi.org/10.1117/12.2651958

KEYWORDS: Biopsy, Lymphatic system, Breast cancer, Receptors, Tumors, Machine learning, Spatial analysis, Microscopy, Luminescence, Diagnostics

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Proceedings Article | 4 March 2022 Presentation + Paper

Deep learning to detect lymphocytes with high phenotypic resolution in highly multiplexed fluorescence microscopy images of triple-negative breast cancer biopsies

Madeleine Durkee, Rebecca Abraham, Marcus Clark, Maryellen Giger

Proceedings Volume 11964, 1196406 (2022) https://doi.org/10.1117/12.2610260

KEYWORDS: Image segmentation, Biopsy, Microscopy, Luminescence, Image resolution, Breast cancer, Tumors, Multiplexing, Tissues, Confocal microscopy

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Proceedings Article | 5 March 2021 Presentation + Paper

Comparing Mask R-CNN and U-Net architectures for robust automatic segmentation of immune cells in immunofluorescence images of Lupus Nephritis biopsies

Madeleine Durkee, Rebecca Abraham, Junting Ai, Jordan Fuhrman, Marcus Clark, Maryellen Giger

Proceedings Volume 11647, 116470X (2021) https://doi.org/10.1117/12.2577785

KEYWORDS: Image segmentation, Biopsy, Therapeutics, Microscopes, Luminescence, Kidney, Inflammation, Convolutional neural networks, Confocal microscopy

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Proceedings Article | 5 March 2021 Presentation

Application of U-Nets for the automatic segmentation of immune cells in high-dimensional microscopy images of kidney biopsies

Rebecca Abraham, Madeleine Durkee, Margaret Veselits, Junting Ai, Jordan Fuhrman, Marcus Clark, Maryellen Giger

Proceedings Volume 11647, 116470N (2021) https://doi.org/10.1117/12.2577788

KEYWORDS: Image segmentation, Kidney, Biopsy, Microscopy, Tissues, Machine vision, Luminescence, Data mining, Convolutional neural networks, Confocal microscopy

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Proceedings Article | 15 February 2021 Poster + Paper

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

Madeleine Durkee, Rebecca Abraham, Junting Ai, Marcus Clark, Maryellen Giger

Proceedings Volume 11603, 1160313 (2021) https://doi.org/10.1117/12.2581060

KEYWORDS: Image segmentation, Biopsy, Kidney, Spatial analysis, Pathogens, Luminescence, Image filtering, Failure analysis, Convolutional neural networks, Confocal microscopy

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Application and generalizability of U-Net segmentation of immune cells in inflamed tissue

Rebecca Abraham, Madeleine Durkee, Margaret Veselits, Junting Ai, Jordan Fuhrman, Marcus Clark, Maryellen Giger

Proceedings Volume 11603, 1160317 (2021) https://doi.org/10.1117/12.2581063

KEYWORDS: Tissues, Image segmentation, Machine vision, Biopsy, Visualization, Tumors, Pathology, Machine learning, Kidney, Confocal microscopy

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Proceedings Article | 15 February 2021 Presentation

Radiomic texture analysis of immunofluorescence images of lupus nephritis biopsies to predict patient progression to end-stage renal disease

Bradie Ferguson, Madeleine Durkee, Rebecca Abraham, Junting Ai, Hui Li, Li Lan, Julian Bertini, Marcus Clark, Maryellen Giger

Proceedings Volume 11603, 116030E (2021) https://doi.org/10.1117/12.2582034

KEYWORDS: Biopsy, Image analysis, Biological research, Kidney, Receivers, Luminescence, Inflammation, Image classification, Convolutional neural networks, Confocal microscopy

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SPIE Journal Paper | 8 January 2021 Open Access

Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis

Madeleine Durkee, Rebecca Abraham, Junting Ai, Margaret Veselits, Marcus Clark, Maryellen Giger

JBO, Vol. 26, Issue 02, 022910, (January 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.2.022910

KEYWORDS: Image segmentation, Biopsy, Tissues, Kidney, Machine vision, Computer vision technology, Network architectures, Luminescence, Confocal microscopy, Microscopes

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Proceedings Article | 16 March 2020 Paper

Preliminary radiomic texture analysis of high-channel fluorescence confocal images of triple-negative breast cancer biopsies

Madeleine Durkee, Bradie Ferguson, Rebecca Abraham, Li Lan, Hui Li, Marcus Clark, Maryellen Giger

Proceedings Volume 11320, 1132013 (2020) https://doi.org/10.1117/12.2549755

KEYWORDS: Tissues, Biopsy, Breast cancer, Inflammation, Confocal microscopy, Statistical analysis, Image classification, Image analysis

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Proceedings Article | 16 March 2020 Paper

Improved instance segmentation of immune cells in human lupus nephritis biopsies with Mask R-CNN

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

Proceedings Volume 11320, 1132019 (2020) https://doi.org/10.1117/12.2549751

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

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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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Proceedings Article | 6 March 2018 Paper

Simultaneous segmentation and classification of multichannel immuno-fluorescently labeled confocal microscopy images using deep convolutional neural networks

Adam Sibley, Vladimir Liarski, Marcus Clark, Maryellen Giger

Proceedings Volume 10581, 1058110 (2018) https://doi.org/10.1117/12.2292934

KEYWORDS: Image segmentation, Confocal microscopy, Image classification, Convolutional neural networks, Kidney, Biopsy, Image quality, Image resolution, Convolution

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The feasibility of localizing, segmenting, and classifying individual cells in multi-channel confocal microscopy images is highly dependent on image quality. In certain applications, with good image quality, the segmentation can be trivial and accomplished via thresholding, watershed, or a collection of other well-established and studied heuristics; however, at the limit of poor image quality and complex image features, these techniques fail. It is at this limit that deep convolutional neural network (DCNN) approaches excel. Our research studies the interaction of individual immune cells and their shape changes relative to inflammatory immune reactions¹ using multi-channel immunofluorescence imaging of renal biopsies from patients with inflammatory kidney disease. We present here a deep learning methodology for application to nuclear and cell membrane immunofluorescent stains to automatically segment and classify multiple T-cell and dendritic cell types. With both T-cells and dendritic cells segmented, we are able to study how T-cells with different surface antigens change shape with proximity to dendritic cells. Shape changes are seen when T-cells move close to dendritic cells and interact. We use a sliding window, max-filtering DCNN to segment and classify 3 cell types from 6 image stains channels within a single DCNN. This DCNN maintains images at original resolution throughout the network using dilated convolutions and max-filtering in place of max pooling layers. In addition, we use 3D convolution kernels with two spatial dimensions and one channel dimension. This allows us to output a multi-class binary classification based on multichannel data at the original image resolution. We trained and validated the network across 24 patients with 8,572 segmented cells. Our results demonstrate a mean Dice-Sorensen score of 0.78 +/- 0.18, a mean classification sensitivity of 0.76, and a mean classification specificity of 0.75 across all 3 segmented cell types.

Proceedings Article | 23 February 2012 Paper

Computerized image analysis of cell-cell interactions in human renal tissue by using multi-channel immunoflourescent confocal microscopy

Yahui Peng, Yulei Jiang, Vladimir Liarski, Natalya Kaverina, Marcus Clark, Maryellen Giger

Proceedings Volume 8315, 83153F (2012) https://doi.org/10.1117/12.910373

KEYWORDS: Image segmentation, Tissues, Image analysis, Visualization, Confocal microscopy, Databases, Biopsy, Digital filtering, Nonuniformity corrections, Analytical research

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Showing 5 of 21 publications

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