A community-based approach to image analysis of cells, tissues and tumors

• Authors: Juan Carlos Vizcarra, Georgia Institute of Technology; Erik A Burlingame, OHSU School of Medicine; Clemens B Hug, Harvard Medical School; Yury Goltsev, Stanford University School of Medicine; Brian S White, Sage Bionetworks; Darren R Tyson, Vanderbilt University School of Medicine; Artem Sokolov, Harvard Medical School
• Journal Title: Computerized Medical Imaging and Graphics
• Volume: Volume 95
• Publisher: Elsevier Inc | 2022-01-01, Pages 102013-102013
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.compmedimag.2021.102013
• Subjects: Engineering, Biomedical; Biology, Microbiology; Health Sciences, Immunology
• Keywords: Cell type calling; Image analysis; Image registration; Image segmentation; Diagnostic Imaging; Humans; Image Processing, Computer-Assisted; Neoplasms; Software; Tumor Microenvironment

Abstract

Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on “Computational Challenges Shared by Diverse Imaging Platforms” to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).