Biomedical Imaging Specialist joins the Clinical Image Lab

Dr. Sufyan Ababneh has joined the clinical image analysis group as a Biomedical Imaging Specialist. Dr. Ababneh received his BS degree in Electrical and Computer Engineering (ECE) from Jordan University of Science and Technology and his MS Degree in ECE from the University of Alabama in Huntsville. In 2008, he received his PhD degree in ECE from the University of Illinois in Chicago. Prior to that, he was a professional working for several well known companies in the private sector. Prior to joining the Ohio State University, he worked from 2002 to 2006, as a senior developer at Toshiba Medical Research Institute USA Inc and Bio-Imaging Research Inc developing CT-Scan imaging systems. From 1998 to 2002, he served as a senior software engineer at Motorola Inc. From 1997 to 1998, he worked as a Development Consultant for Bio-Imaging Research Inc. From 1995 to 1997, he worked as an Algorithms Developer designing embedded-systems applications at Circuit City.

Dr. Ababneh's research interests include image analysis, segmentation, classification, 2-D and 3-D compression with applications to medical images and telemedicine, image informatics and computer-aided diagnosis. He spent five years developing high performance CT-scan bio-imaging systems in distributed and embedded environments. In addition, he conducted research on watermarking-based multimedia content authentication.

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CIALAB encouraging talented young minds with summer internships

CIALAB is pleased to introduce the three interns namely Tong Gan, Rosana Rodriguez Milanes and Michael Priddy working through summer’09. Rosana Rodriguez Milanes - I am a third year undergraduate student in Electronic Engineering from Universidad del Norte, Colombia. My experience as a volunteer foreign student in the Clinical Image Analysis Laboratory has been an edifying, gratifying and enriching. Being able to participate, to learn and to collaborate in the Clinical Image Analysis Laboratory during the past two weeks has allowed me to improve my analytical and interpretative skills in processing histopathological and MRI images. I have been able to learn about segmentation, region growing, splitting and merging algorithms development. I have also had the privilege of knowing and interacting with excellent engineers who have helped me improve my skills as a foreign student. I am grateful for the opportunity that the Ohio State University has given me to collaborate and to learn with

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

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. Segmentation a

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