Recent work in pattern recognition has demonstrated that computers can equal or even surpass image classification and pattern analysis by human experts. Modern imaging systems far exceed the human eye in spatial and spectral resolution as well as dynamic range, thus potentially allowing machine-based image pattern analysis systems to perform such tasks. The pattern analysis system we have developed, characterized and published is called WND-CHARM. A key property of WND-CHARM is that it can provide quantitative measures of image similarity. Quantitative comparisons between individual images or groups of images allow the establishment of a time-course for the progression of a physiological process represented by images. An independent verification that the continuous evolution of morphological patterns in a process is detected by the computer is its ability to place these images in order without a priori information. Subsequently, the degree of change from one timepoint to another can be used to determine if the physiological process is smooth and continuous or progresses through discrete stages. Discontinuous processes imply the presence of biological control at the transitions, and may point to key stages for medical interventions. Our published work has shown that age-related muscle degeneration (sarcopenia) in the C. elegans pharynx occurs in three discrete stages. This was the first characterization of discrete post-developmental morphological states in any organism. The observation that muscle degeneration occurs in stages implies that it may be a regulated process, and may be subject to intervention to prevent or delay these transitions. Our current work will investigate if these stages can be consistently observed in mammalian tissues, and whether interventions such as diet and the drug resveratrol affects the timing or magnitude of these morphological changes. We have also published work investigating the progression of osteoarthritis (OA) in the human population comprising the Baltimore Longitudinal Study of Aging (BLSA). We were able to show that WND-CHARM is able to diagnose the existence of OA in knee X-Rays with accuracies approaching that of a panel of highly trained radiologists. More recently, we have published work that WND-CHARM can predict the future onset of radiologically detectable osteoarthritis in X-Rays that were scored as radiologically clear. We were able to show that the development of moderate OA two decades in the future can be predicted with >70% accuracy from an X-Ray scored as free of OA by a panel of three radiologists. Recently, we were able to further characterize OA progression and identify an early, slow period of change followed by rapid degeneration. We are following up these studies with an MRI dataset we obtained from the Osteoarthritis Initiative. Our work with processing images of H&E-stained sections of cancer biopsies has shown that consistency of sectioning and staining is a key factor in effective image analysis of medical samples. More recently we were able to demonstrate that the common approach to the use of color-sensitive image features is not as effective as deconvolving the Hematoxylin and Eosin stains into separate channels in a pre-processing step and subsequently treating them as separate grayscale channels. Additionally we analyzed many commonly used image-feature and classification algorithms to determine their relative effectiveness as well as propose strategies for improving classifier performance. These studies have been published in the previous year. Our success diagnosing and sub-classifying melanoma metastases has led us to expand this analysis to a survey of several types of cancers available as commercial tissue microarrays. Our current work focuses on studying physiological age in several human tissues available through the BLSA and the Osteoarthritis Initiative (OAI). A question we are addressing is whether aging progresses through distinguishable states as we have observed in C. elegans, and the degree to which physiological age progresses synchronously in different tissues within individuals.The tissues we are studying are primarily muscle and bone, imaged using several modalities (CT, MRI, histology). The characterization of the onset of frailty is one of the potential outcomes of these studies, as well as potentially characterizing segmental aging. The availability of longitudinal data from the BLSA and other studies will allow us to determine the degree to which these life transitions are predictable from non-invasive imaging techniques, similarly to how we were able to predict the onset of osteoarthritis in later life.
|Ashinsky, B G; Coletta, C E; Bouhrara, M et al. (2015) Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging. Osteoarthritis Cartilage 23:1704-12|
|Orlov, Nikita V; Eckley, D Mark; Shamir, Lior et al. (2012) Improving class separability using extended pixel planes: a comparative study. Mach Vis Appl 23:1047-1058|
|Orlov, Nikita V; Weeraratna, Ashani T; Hewitt, Stephen M et al. (2012) Automatic detection of melanoma progression by histological analysis of secondary sites. Cytometry A 81:364-73|