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.
Brandon Ginley, Tiffany Emmons, Prabhu Sasankan, Constantin Urban, Brahm H. Segal, and Pinaki Sarder, "Identification and characterization of neutrophil extracellular trap shapes in flow cytometry," Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400D (Presented at SPIE Medical Imaging: February 13, 2017; Published: 1 March 2017); https://doi.org/10.1117/12.2254680.
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