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Identification and characterization of neutrophil extracellular trap shapes in flow cytometry
Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Immunology/Immunchemistry.
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2017 (English)In: Medical Imaging 2017: Digital Pathology / [ed] Gurcan, MN Tomaszewski, JE, 2017, 101400DConference paper, Published paper (Refereed)
Abstract [en]

Neutrophil extracellular trap (NET) formation is an alternate immunologic weapon used mainly by neutrophils. Chromatin backbones fused with proteins derived from granules are shot like projectiles onto foreign invaders. It is thought that this mechanism is highly anti-microbial, aids in preventing bacterial dissemination, is used to break down structures several sizes larger than neutrophils themselves, and may have several more uses yet unknown. NETs have been implied to be involved in a wide array of systemic host immune defenses, including sepsis, autoimmune diseases, and cancer. Existing methods used to visually quantify NETotic versus non-NETotic shapes are extremely time-consuming and subject to user bias. These limitations are obstacles to developing NETs as prognostic biomarkers and therapeutic targets. We propose an automated pipeline for quantitatively detecting neutrophil and NET shapes captured using a flow cytometry-imaging system. Our method uses contrast limited adaptive histogram equalization to improve signal intensity in dimly illuminated NETs. From the contrast improved image, fixed value thresholding is applied to convert the image to binary. Feature extraction is performed on the resulting binary image, by calculating region properties of the resulting foreground structures. Classification of the resulting features is performed using Support Vector Machine. Our method classifies NETs from neutrophils without traps at 0.97/0.96 sensitivity/specificity on n = 387 images, and is 1500X faster than manual classification, per sample. Our method can be extended to rapidly analyze whole-slide immunofluorescence tissue images for NET classification, and has potential to streamline the quantification of NETs for patients with diseases associated with cancer and autoimmunity.

Place, publisher, year, edition, pages
2017. 101400D
Series
Proceedings of SPIE, ISSN 0277-786X ; 10140
Keyword [en]
Neutrophil extracellular trap, support vector machine, flow cytometry, image analysis
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:umu:diva-138058DOI: 10.1117/12.2254680ISI: 000404880200012ISBN: 978-1-5106-0725-5 (print)ISBN: 978-1-5106-0726-2 (electronic)OAI: oai:DiVA.org:umu-138058DiVA: diva2:1129426
Conference
5th Digital Pathology Conference, FEB 12-13, 2017, Orlando, FL
Available from: 2017-08-03 Created: 2017-08-03 Last updated: 2017-08-03Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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