Clinical Image Analysis

Thesis Title : Computer-Assisted Discovery and Characterization of Imaging Biomarkers for Disease Diagnosis and Treatment Planning Abstract: The rapid growth of diagnostic medical imaging studies has led to enormous strides in the effective diagnosis and treatment of myriad diseases, from chronic diseases to life threatening cancers. The rise of imaging as a major factor in medical decision making has directly led to a drive towards quantification of image findings, in order to augment the qualitative analysis of trained medical professionals, such as radiologists. The overarching goal of this dissertation is to explore, develop, and evaluate imaging biomarkers for both chronic and life threatening diseases. Towards this goal, this dissertation has the following two aims: 1. Discover and characterize imaging biomarkers for diseases which may have either acute/sub-acute presentation or treatment, or diseases which may have a more chronic course and treatment intervention. The former ana

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 st

CIALAB research on developing an automated computer-assisted system for follicular lymphoma grading has been accepted for publication in IEEE Transactions on Biomedical Engineering's forthcoming special issue on Multi-Parameter Optical Imaging and Image Analysis. Information regarding the publication is below: Title: Computer-aided Detection of Centroblasts for Follicular Lymphoma Grading using Adaptive Likelihood based Cell Segmentation Authors: Sertel O, Lozanski G, Shana'ah A, Gurcan MN Abstract: Follicular lymphoma (FL), is one of the most common lymphoid malignancies in the western world. FL has a variable clinical course and important clinical treatment decisions for FL patients are based on histological grading, which is done by manually counting the large malignant cells called centroblasts (CB) in ten standard microscopic high power fields from H&E-stained tissue sections. This method is tedious and subjective; as a result suffers from considerable inter- and in