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Proceedings Paper

Computerized microscopic image analysis of follicular lymphoma
Author(s): Olcay Sertel; Jun Kong; Gerard Lozanski; Umit Catalyurek; Joel H. Saltz; Metin N. Gurcan
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Paper Abstract

Follicular Lymphoma (FL) is a cancer arising from the lymphatic system. Originating from follicle center B cells, FL is mainly comprised of centrocytes (usually middle-to-small sized cells) and centroblasts (relatively large malignant cells). According to the World Health Organization's recommendations, there are three histological grades of FL characterized by the number of centroblasts per high-power field (hpf) of area 0.159 mm2. In current practice, these cells are manually counted from ten representative fields of follicles after visual examination of hematoxylin and eosin (H&E) stained slides by pathologists. Several studies clearly demonstrate the poor reproducibility of this grading system with very low inter-reader agreement. In this study, we are developing a computerized system to assist pathologists with this process. A hybrid approach that combines information from several slides with different stains has been developed. Thus, follicles are first detected from digitized microscopy images with immunohistochemistry (IHC) stains, (i.e., CD10 and CD20). The average sensitivity and specificity of the follicle detection tested on 30 images at 2×, 4× and 8× magnifications are 85.5±9.8% and 92.5±4.0%, respectively. Since the centroblasts detection is carried out in the H&E-stained slides, the follicles in the IHC-stained images are mapped to H&E-stained counterparts. To evaluate the centroblast differentiation capabilities of the system, 11 hpf images have been marked by an experienced pathologist who identified 41 centroblast cells and 53 non-centroblast cells. A non-supervised clustering process differentiates the centroblast cells from noncentroblast cells, resulting in 92.68% sensitivity and 90.57% specificity.

Paper Details

Date Published: 17 March 2008
PDF: 11 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691535 (17 March 2008); doi: 10.1117/12.770936
Show Author Affiliations
Olcay Sertel, The Ohio State Univ. (United States)
Jun Kong, The Ohio State Univ. (United States)
Gerard Lozanski, The Ohio State Univ. (United States)
Umit Catalyurek, The Ohio State Univ. (United States)
Joel H. Saltz, The Ohio State Univ. (United States)
Metin N. Gurcan, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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