title: Detection of follicles from IHC-stained slides of follicular lymphoma using iterative watershed.
abstract: Follicular lymphoma (FL) is one of the most common types of nonHodgkin lymphoma in the U.S. Diagnosis of FL is based on tissue biopsy that shows characteristic morphologic and immunohistochemical (IHC) findings. Our group's work focuses on the development of computer-aided image-analysis techniques to improve the FL grading. Since centroblast enumeration needs to be performed in malignant follicles, the development of an automated system to accurately identify follicles on digital images of lymphoid tissue is an important step. In this letter, we describe an automated system to identify follicles in IHC-stained tissue sections. A unique feature of the system described here is the use of texture and color information to mimic the process that a human expert might use to identify follicle regions. Comparison of system-generated results with expert-generated ground truth has shown promising results with a mean similarity score of 87.11%.
authors.affiliation: Ohio Supercomputer Center, Columbus, OH 43212, USA. samsi.1@osu.edu
authors.forename: Siddharth
authors.initials: S
authors.lastname: Samsi
authors.forename: Gerard
authors.initials: G
authors.lastname: Lozanski
authors.forename: Arwa
authors.initials: A
authors.lastname: Shana'ah
authors.forename: Ashok K
authors.initials: AK
authors.lastname: Krishanmurthy
authors.forename: Metin N
authors.initials: MN
authors.lastname: Gurcan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry / Q000379:methods*; D008224:Lymphoma, Follicular / Q000473:pathology*