Abstract
Which drug is most promising for a cancer patient? A new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer this question in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, adjusting filters, removing noise, and analyzing the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both derived data such as growth rates and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach for choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.
Citation
Devin Lange,
Eddie Polanco,
Robert Judson-Torres,
Thomas A. Zangle,
Alexander Lex
Loon: Using Exemplars to Visualize Large-Scale Microscopy Data
IEEE Transactions on Visualization and Computer Graphics (VIS), 28(1): 248-258, doi:10.1109/TVCG.2021.3114766, 2021.
IEEE VIS 2021 Honorable Mention Award
BibTeX
@article{2021_vis_loon,
title = {Loon: Using Exemplars to Visualize Large-Scale Microscopy Data},
author = {Devin Lange and Eddie Polanco and Robert Judson-Torres and Thomas A. Zangle and Alexander Lex},
journal = {IEEE Transactions on Visualization and Computer Graphics (VIS)},
publisher = {IEEE},
doi = {10.1109/TVCG.2021.3114766},
volume = {28},
number = {1},
pages = {248-258},
year = {2021}
}
Acknowledgements
We wish to thank Tarek Moustafa and Jack Wilburn for their contributions, and Soorya Pradeep and Jingzhou Zhang for participating in the CVO workshop. The research reported in this publication was supported by Huntsman Cancer Foundation through the Computational Oncology Research Initiative and the University of Utah’s 1U4U seed grants and the Office of the Assistant Secretary of Defense for Health Affairs through the Breast Cancer Research Program under Award No. W81XWH-19-10065.
Images
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