A New Way to Look at an Old Disease

CIA Lab Researchers, in collaboration with the Department of Infectious Disease and Global Health, Tufts University in Grafton, Massachusetts, have developed a novel platform to analyze tuberculosis histopathology.

The recent publication, "Detecting and Characterizing Cellular Responses to Mycobacterium Tuberculosis from Histology Slides” by Niazi, Beamer, and Gurcan, details a new method to identify and characterize granulomas and lymphocytic cuffs in Mycobacterium tuberculosis infected lungs. The framework, DeHiDe, used an internuclei geodesic distance calculation to detect the high cell density regions at 99.39% accuracy compared to the pathologist and to classify the regions at 90.87% accuracy. The full text will be published in the February 2014 edition of Cytometry A.

One third of the world's population is thought to have been infected with tuberculosis causing bacteria, and there are an estimated 14 million active chronic cases all over the world.

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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 ...

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

Clinical Image Analysis

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