Immunohistochemical detection of FOXP3 antigen is a usable marker for detection of regulatory T lymphocytes (TR) in
formalin fixed and paraffin embedded sections of different types of tumor tissue. TR plays a major role in homeostasis
of normal immune systems where they prevent auto reactivity of the immune system towards the host. This beneficial
effect of TR is frequently “hijacked” by malignant cells where tumor-infiltrating regulatory T cells are recruited by the
malignant nuclei to inhibit the beneficial immune response of the host against the tumor cells. In the majority of human
solid tumors, an increased number of tumor-infiltrating FOXP3 positive TR is associated with worse outcome. However,
in follicular lymphoma (FL) the impact of the number and distribution of TR on the outcome still remains controversial.
In this study, we present a novel method to detect and enumerate nuclei from FOXP3 stained images of FL biopsies. The
proposed method defines a new adaptive thresholding procedure, namely the optimal adaptive thresholding (OAT)
method, which aims to minimize under-segmented and over-segmented nuclei for coarse segmentation. Next, we
integrate a parameter free elliptical arc and line segment detector (ELSD) as additional information to refine
segmentation results and to split most of the merged nuclei. Finally, we utilize a state-of-the-art super-pixel method,
Simple Linear Iterative Clustering (SLIC) to split the rest of the merged nuclei. Our dataset consists of 13 region-ofinterest
images containing 769 negative and 88 positive nuclei. Three expert pathologists evaluated the method and
reported sensitivity values in detecting negative and positive nuclei ranging from 83-100% and 90-95%, and precision
values of 98-100% and 99-100%, respectively. The proposed solution can be used to investigate the impact of FOXP3
positive nuclei on the outcome and prognosis in FL.
C. Senaras, M. Pennell, W. Chen, B. Sahiner, A. Shana’ah, A. Louissaint, R. P. Hasserjian, G. Lozanski, and M. N. Gurcan, "FOXP3-stained image analysis for follicular lymphoma: optimal adaptive thresholding with maximal nucleus coverage," Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400E (Presented at SPIE Medical Imaging: February 13, 2017; Published: 1 March 2017); https://doi.org/10.1117/12.2255671.
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