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Image analysis to solve the mystery of breast cancer therapy


Clinical Image Analysis Lab (CIALAB) researchers at The Ohio State University have developed quantitative image analysis algorithms to help better their understanding of how certain breast cancer therapies work. These types of algorithms, already shown be safe and effective to help doctors in breast cancer detection, can also help researchers unlock the mystery of how certain treatments work. The methodology and findings were published in the latest issue of PLOS ONE  (http://bit.ly/188kFpA).

Both radiological and microscopic imaging plays a key role in detection, diagnosis, and treatment of breast cancer as well as developing effective therapies against it. Typically, these images are analyzed by human readers (e.g. radiologist, pathologist) to detect disease patterns or to quantify changes. Due to variation between different readers, these results are not always accurate or consistent. That’s where computers come into play. Artificial intelligence and computer vision technologies are now employed to quantitatively analyze images. Unlike human readers, computer programs can analyze large quantities of images with high accuracy and consistency at high speed without getting tired.

For this study, CIALAB researchers developed computer analysis algorithms to quantitatively analyze microscopic images to determine the level of apoptosis (cell death), a key indicator in cancer studies. Director of the lab, Dr. Gurcan, said, “These algorithms will change the way we conduct cancer research by reducing inter- and intra-reader variability, a major source of errors.” Working with Dr. Gurcan, Dr. Niazi, a postdoctoral researcher in the lab, developed a novel cell detection algorithm to identify positively stained cells that are indicative of apoptosis. The CIALAB researchers collaborated with Dr. Das’ research group at The Ohio State University and Dr. Cristini’s group at the University of New Mexico. This collaboration brought together a unique combination of expertise in immunotherapy, mathematical modeling, and computerized image analysis at these two different institutions. 

 It is expected that these types of algorithms will become an integral part of all biological and medical research. As the amount of data, particularly images, increases exponentially, it is becoming harder and harder for human readers to continue to use their eyes to make accurate and consistent decisions about the image content. To help with image analysis algorithm development, large, well-annotated libraries of images are being created. These images, combined with the information in the literature and domain knowledge of experts are all turned into effective image analysis computer algorithms.  CIALAB is one of the leading research groups in the development of image analysis algorithms in medicine. 


    Further information about Clinical Image Analysis Lab can be found at www.bmi.osu.edu/cialab.

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