Skip to main content

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. The cancer risk is initially defined by cancer invasion into the bladder wall. Tumor invasion into the first layer is termed stage T1. It may be difficult to confirm and sub-stage T1 cancer because of the difficulty in recognizing all of the anatomic landmarks. This difficulty makes it challenging for urologists to recommend the right treatment. The proposed study will develop image analysis tools to stage and risk stratify patients accurately, which will help urologists make better treatment decisions.  


BMI Image Analysis Lab (http://www.bmi.osu.edu/cialab) will be developing the image analysis algorithms and software for the project. Founded in 2006 by Dr. Gurcan, the CIALAB’s mission is to develop image analysis algorithms to improve human health and well-being while training future research leaders in this field. The qualitative analysis of histopathological, radiological and dermoscopic images is time-consuming and subject to inter- and intra-reader variations. These variations may negatively affect the clinical outcome. The CIALAB has specialized in developing image analysis systems for computer-assisted interpretation of medical images. The research in the lab has been supported by the National Cancer Institute, National Institute of Allergy and Infectious Diseases, National Library of Medicine, American Cancer Society, Department of Defense, National Rosacea Society, American Acne and Rosacea Society, American Lung Association, National Football League Charities, The Children’s Neuroblastoma Cancer Foundation, and the European Union.


Pelotonia Awardee Dr. Metin Gurcan riding his bike in Pelotonia 2016 with one goal: “End Cancer.”

Comments

  1. Good post,Consistently something going to not be right and I would nothing be able to successfully spare the valuable life but you can get click on this page for best work. When everything is going great and all of a sudden I tuned in to the awful news. All the satisfaction is changing into bleak and feeling of unexplained. Same here in this news occurred.

    ReplyDelete
  2. Good post,This is extraordinary to understand that you give the short introduction about yourself. Nowadays, everybody needs to consider the all inclusive community who eminent or exceptional person but you can visit http://www.road2residency.com/fellowship-personal-statement-services/ to manage your task. This is so short preamble to you and I have to get some answers concerning your consistently plan penchant.

    ReplyDelete
  3. There is huge competition in psychiatry residency. Students always need special tips for passing the competition. Here http://www.cspersonalstatements.com/full-guide-to-cardiology-fellowship-personal-statement-writing/ you can find the top ten important tips for psychiatry residency personal statement.

    ReplyDelete

Post a Comment

Popular posts from this blog

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 ...

Drs. Goceri, Kus, and Senaras Present at OSUMC Research Day

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...

Computer’s help in wound image analysis

Computers help in wound image analysis CIALAB’s work in collaboration with the OSUMC Wound Center researchers entitled, “ Computerized Segmentation and Measurement of Chronic Wound Images” has been accepted to the journal of Computers in Biology and Medicine (CBM). The objective of this study is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. Further details about the paper can be found at the CBM website: www.computersinbiologyandmedicine.com/article/S0010-4825(15)00064-5/abstract