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

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# [Development of a high-throughput image processing pipeline for multiplex immunofluorescence whole slide images at scale](https://www.pathai.com/resources/development-of-a-high-throughput-image-processing-pipeline-for-multiplex-immunofluorescence-whole-slide-images-at-scale)

 Written by [Admin](https://www.pathai.com/resources/author/admin) | Apr 14, 2023 4:00:00 AM

## 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.

 Waleed Tahir 1,*, Emma Krause 1,*, Judy Shen 1, Vignesh Valaboju 1, Jin Li 1, Howard Mak 1, Mohammad Mirzadeh 1, Kevin Rose 1, Guillaume Chhor 1, Joseph Lee 1, Jun Zhang 1, Jacqueline Brosnan-Cashman 1, Michael G. Drage 1, Justin Lee 1, Carlee Hemphill 2, Saumya Pant 2, Robert Egger 1

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

[View Poster](https://www.pathai.com/hubfs/2025%20Assets/AACR_mIF.pdf)

[View full post](https://www.pathai.com/resources/development-of-a-high-throughput-image-processing-pipeline-for-multiplex-immunofluorescence-whole-slide-images-at-scale)

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