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Recent publications

The CIA lab has recently had 4 articles published in PLOS One and the Journal of Urology. Automated Staging Of T1 Bladder Cancer Using Digital Pathologic H&E Images: A Deep Learning approach (Journal of Urology). The paper discusses the need for accurately gauging tumor cell intrusion into Lamina Propria in an effort to substage bladder cancer. It explains how transfer learning in conjunction with Convolutional Neural Networks can be used to accurately identify different bladder layers and then compute the distance between tumor nuclei and Lamina Propria. The article is available here:  https://www.jurology.com/article/S0022-5347(18)41148-2/pdf Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning (PLOS One). This paper examines a proposed methodology to automatically differentiate between NET and non-tumor regions based on images of Ki67 stained biopsies. It also uses transfer learning to exploit a rich set of features developed
Recent posts

CIALAB has moved to Wake Forest

The Clinical Image Analysis Lab (CIALAB) that was started by Dr. Metin Gurcan in 2005 at the Ohio State University has moved to Wake Forest School of Medicine. The new lab at Wake Forest will retain its name, but its web page will now be located at : http://tsi.wakehealth.edu/CIALab/   Dr. Gurcan was recruited as the inaugural Director of the Center for Biomedical Informatics at Wake Forest School of Medicine. The newly established Center will strengthen Wake Forest’s infrastructures for informatics research in genomics, imaging, healthcare delivery, and population health and support the informatics needs of other research centers. In so doing, the Center will promote Wake Forest Baptist Health's evolution as a learning health care system. A further goal of the Center is to train the next generation of investigators in the principles and practice of biomedical informatics.   The CIALab has been one of the leading research labs in the world and known for its innovative and trans

BMI researchers to receive prestigious Intramural Award

BMI researcher Metin Gurcan, PhD (Professor, Director of Division of Clinical and Translational Informatics) in collaboration with Anil Parwani, MD, PhD, MBA (Vice Chair of Pathology) and Cheryl Lee, MD (Chair of Urology) have been awarded one of the prestigious OSU Comprehensive Cancer Center Intramural Research Funding Awards. The research team for the project also includes Soledad Fernandez, PhD (BMI), Nancy Single (CCC), Khalid Niazi, PhD (BMI) and Brett Klamer, MS (BMI).   The two-year project, entitled Application of image analysis tools to accurately stage and risk stratify patients with T1 bladder cancer, will be primarily funded by Pelotonia dollars. Pelotonia is a three-day bike tour organized every year in Columbus to raise money for cancer research with one goal: “End Cancer.” Every rider-raised dollar goes to fund research at The Ohio State University Comprehensive Cancer Center. Bladder cancer is an important disease that affects nearly 77,000 people annually. T

Student volunteer, Aashish B. Katapadi Published in the Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization Journal

Student volunteer, Aashish B. Katapadi (a first year medical student accepted to the medical school at OSU) and Dr. Gurcan have been recently published in the Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization Journal , 2017 edition. Their paper, "Evolving strategies for the development and evaluation of a computerised melanoma image analysis system" discussed the development of such a tool that served as an early detector for the rising incidence of melanoma. In addition, the creation of an analysis classifier was considered for further analysis and the findings were shared within the paper. The abstract and full paper may be accessed through the following link: http://www.tandfonline.com/doi/full/10.1080/21681163.2016.1277785

Dr. Gurcan Published in the Journal of Biomedical Informatics

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

Dr. Niazi Accepted in the IEEE Journal of Biomedical and Health Informatics

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