Dr. Gurcan is associate editor of a special issue on Whole Slide Microscopy Analysis

Recent advances in technological solutions for automated high-speed and high-resolution whole slide imaging (WSI) have set the basis for a digital revolution in microscopy. This ability to observe and analyze entire specimens rather that single microscopic fields of view is affecting the way microscopic evaluation is practiced. However, WSI outputs quite huge multiple channel (at least three color channels) images (e.g. 30-40 GB) for a single slide and managing such amount of data is a unique challenge for this new era of digital microscopy. Currently, WSI workstations are mainly used to perform virtual microscopy, the practice of converting entire glass slides into high-resolution digital slides that can be viewed and managed across networks.

The aim of the proposed special issue is to present some of the cutting-edge works currently being done in Whole Slide Imaging and reveal the challenges that still lie ahead. The special issue will be a mix of invited and solicited papers. A perspective editorial written by the special issue guest editors will introduce the technology; describe potential applications and pitfalls. Invited papers are intended to provide reviews both from the medical and the image processing sides.

Further information can be found at: http://greyc.stlo.unicaen.fr/lezoray/CMIG-CFP/

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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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