QCHARM: A Novel Computational and Scientific Visualization Framework for Facilitating Discovery and Improving Diagnostic Reliability in Medicine

Histopathologists microscopically examine tissue samples, sectioned into thin slices, in order to make accurate diagnoses of tumors and other diseases; however, diagnoses based on examinations of a single tissue sample may vary considerably from one histopathologist to the next (or even for individual pathologists, for multiple examinations of the same sample)1. As a result, there are few guarantees about the reliability of such diagnoses, which may lead to critical errors regarding the timing and choice of cancer therapies. In response to this problem, we are developing advanced image recognition methods whose goal is to automatically recognize and quantitatively characterize abnormalities in the tissue morphology of larval and adult zebrafish. The zebrafish has been shown to be an excellent model organism for vertebrate development and human disease, largely because its transparent embryo allows the effects of mutations to be easily identified2. Once we have tested the algorithms to characterize zebrafish tissue morphology, we will extend them to clinical applications such as the histopathology of cancer. These algorithms represent the central element of a novel scientific visualization framework that we are developing. This framework will not only facilitate the ability to explore zebrafish tissue morphology, but will also be compatible with biological databases representing the genomics of other model organisms (rat, mouse, primates, etc.) as well as humans. The annotated genes underlying the abnormalities in zebrafish can thus be compared against analogous genes in other species. The existence of similar traits in other species will provide evidence of the function of the analogous gene in humans. This can lead to an improved understanding of human development and also drive more targeted development of disease treatments.