CIALAB research on the detection of follicles in follicular lymhpoma has been accepted for publication in TBME Letters. Information regarding the publication is found below:
Title: Detection of Follicles from IHC Stained Slides of Follicular Lymphoma Using Iterative Watershed
Authors: Siddharth Samsi, Gerard Lozanski, Arwa Shana'ah, Ashok K Krishanmurthy, Metin N. Gurcan
Abstract: Follicular Lymphoma (FL) is one of the most com- mon types of non-Hodgkin Lymphoma in the United States. Diagnosis of FL is based on tissue biopsy that shows characteristic morphologic and immunohistochemical findings. Our group’s work focuses on development of computer-aided image analysis techniques to improve 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 paper 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%.
Title: Detection of Follicles from IHC Stained Slides of Follicular Lymphoma Using Iterative Watershed
Authors: Siddharth Samsi, Gerard Lozanski, Arwa Shana'ah, Ashok K Krishanmurthy, Metin N. Gurcan
Abstract: Follicular Lymphoma (FL) is one of the most com- mon types of non-Hodgkin Lymphoma in the United States. Diagnosis of FL is based on tissue biopsy that shows characteristic morphologic and immunohistochemical findings. Our group’s work focuses on development of computer-aided image analysis techniques to improve 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 paper 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%.
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