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


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.
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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.