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

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

doi:10.1117/12.2654521

3 April 2023 Convolutional neural networks detect cells in densely packed images at performance levels similar to human readers

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

Author Affiliations +

Proceedings Volume 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment; 124670J (2023) https://doi.org/10.1117/12.2654521
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Deep convolutional neural networks (CNNs) have demonstrated high accuracy in a wide range of computer vision applications, including medical and biological imaging. Many CNNs are fully supervised learning algorithms, and their performance is directly associated with the quality of the training data labels, which are human-defined. In this work, we investigate the fidelity of human-defined truth for cell detection, segmentation, and classification tasks in multiplex microscopy images. We compare manual annotations from human readers on three tasks. Readers were asked to (1) segment all cells in single-channel fluorescence images of a pannuclear stain (DAPI), (2) segment cells in two-channel fluorescence images (CD20/DAPI), only identifying cells with both nuclear signal (DAPI) and signal from a cell surface marker (CD20), and (3) segment two separate cell classes in three-channel fluorescence images (CD3/CD4/DAPI). In this third task, readers were asked to identify cells that had nuclear signal and were CD3+/CD4- and CD3+/CD4+. By comparing these manual segmentations within and between readers, we demonstrate that human readers show the least variability in single-channel DAPI segmentation (p<<0.05, F test for equal variance). We also compared the agreement of human readers with one another to the agreement of an object-detection network, Yolov5, on cell detection in DAPI images. All pairwise comparisons of human readers with other human readers yielded an average F1-score of 0.83±0.14, and comparisons of Yolov5 with human readers yielded an average F1-score of 0.84±0.12 (p=0.26, Welch’s T test). We therefore demonstrate that out of the provided tasks, DAPI detection provides the highest fidelity ground truth, and were unable to show a difference between Yolov5 and human readers in this task.

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Madeleine S. Durkee, Nevaeh Petrie, Kyle Lleras, Junting Ai, Rebecca Abraham, J. Cy Chittenden, Chasity Kasir, Fiona Clark, Gabriel Casella, Marcus R. Clark, and Maryellen L. Giger "Convolutional neural networks detect cells in densely packed images at performance levels similar to human readers", Proc. SPIE 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 124670J (3 April 2023); https://doi.org/10.1117/12.2654521

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