Clinical Image Analysis

Dr. Gurcan has contributed to the newly published book, "Digital Pathology." He has written a chapter on the fundamentals of image analysis. To purchase the textbook, follow the link:  https://www.ascp.org/store/productlisting/productdetail?productId=36433654

Dr. Gurcan has a recently published article in the Journal of Biomedical Informatics , January 2017 edition. His paper "Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem" provides a proposal of developing an ontology, the Quantitative Histopathological Imaging Ontology (or QHIO), to represent the imaging data and methods used in the pathological imaging and analysis. The article goes on to describe the application of QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts. The abstract and full paper may be accessed through the following link:  https://urldefense.proofpoint.com/v2/url?u=https-3A__authors.elsevier.com_a_1UOrB5SMDQR4xe&d=DwIFaQ&c=k9MF1d71ITtkuJx-PdWme51dKbmfPEvxwt8SFEkBfs4&r=lGNNG0YcIKKxA7URcw6oK0Fx3RC7UeUdVNw1R4D2rtY&m=9eMivap4AaTUwDVS-z7P_7CcZMda9T1yQ50v17OjMPg&s=ysXEoJ-PmvLI4062p...

Our recent work, "Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer," has been accepted in the IEEE Journal of Biomedical and Health Informatics (J-BHI). The paper provides the process of introducing a new set of visually meaningful features as a tool to evaluate the prostate risk grading. It explains the importance of the grading of prostate cancer for the determination of the appropriate treatment for an individual. The paper continues to explain how the tool would allow pathologists for further examination of the results in light of any disagreement of the diagnoses. It also discusses the possible further investigations of extending the features by investigating specific patterns, restricting specific regions, and including a greater number of pathologists. The abstract and full document in PDF form can be found at the following website:  http://ieeexplore.ieee.org/ document/7467409/?reload=true& arnumber=7467409

On April 25th, Drs. Goceri, Kus, and Senaras presented their research at the OSUMC Research Day.  Dr. Evgin Goceri presented “Automatic and Robust Segmentation of Liver and Its Vessels from MR Datasets for Pre-Evaluation of Liver Translation” which proposed a robust and fully automated method for segmenting the liver and its vessels from MR images. Dr. Goceri’s study presented a novel approach to reducing processing time by employing binary regularization of the level set function. The fully-automatic segmentation of liver and its vessels with the proposed method was more efficient than manual approach and the other methods in the literature in terms of processing time and accuracy. Dr. Pelin Kus presented “Segmentation and Quantification of Tissue Necrosis in Tuberculosis” which focuses on how the immune system of patients infected with M. tuberculosis responds by using many types of cells including macrophages that form granulomas within the pulmonary tissue. Segmentati...

Drs.  Goceri and Gurcan have a new paper published in Computers in Biology and Medicine, April 2016 edition. Their paper "Quantification of liver fat: A comprehensive review" provides an overview on recent advantages in liver fat quantification and discusses the role of dedicated imaging modalities for quantification of liver fat. Their paper also assesses the potential role of automated image processing methodologies to aid in image analysis. The abstract and full paper can be found at the following website: http://www.computersinbiologyandmedicine.com/article/S0010-4825(16)30042-7/abstract

Dr. Caglar Senaras has joined the CIA Lab as a post doctoral researcher. He received his Ph.D. degree in Information Systems from the Middle East Technical University in Ankara, Turkey in 2013. His main research interests include areas of image processing and pattern recognition. Further information about the Clinical Image Analysis Lab and all its work can be found at www.bmi.osu.edu/cialab/.

Drs. Gurcan and Fauzi are published in BMC Medical Informatics and Decision Making with their paper Classification of follicular lymphoma: the effect of computer aid on pathologists grading. Their paper presents a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. The results of this study show that FLAGS can be useful in increasing the pathologists’ accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists’ grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.