Poster

Development of a high-throughput image processing pipeline for multiplex immunofluorescence whole slide images at scale

AACR

Study Background

Fluorescent imaging technologies now allow for multiplex immunofluorescence (mIF) of up to 100 targets on a single slide. However, the ability to quantitatively analyze the resulting data, especially on whole-slide images (WSI), is limited by scalability and reproducibility.
Currently used platforms for segmenting cancer cells and nuclei involve segmentation algorithms that are hand-tuned on individual fields of view, making these methods subjective and difficult to replicate.
To this end, we sought to develop an end-to-end workflow for WSI mIF data in cancer, from raw images to cell-level features (Fig. 1), using stateof-the-art deep learning models for tissue, cell, and nuclei segmentation.

Authors

Waleed Tahir¹,*, Emma Krause¹,*, Judy Shen¹, Vignesh Valaboju¹, Jin Li¹, Howard Mak¹, Mohammad Mirzadeh¹, Kevin Rose¹, Guillaume Chhor¹, Joseph Lee¹, Jun Zhang¹, Jacqueline Brosnan-Cashman¹, Michael G. Drage¹, Justin Lee¹, Carlee Hemphill², Saumya Pant², Robert Egger¹

¹PathAI, Boston, Massachusetts
²PathAI Biopharma Lab, Memphis, Tennessee
*Contributed equally to this study.