Modern imaging systems far exceed the human eye in spatial and spectral resolution and in dynamic range, thus potentially allowing machine-based image pattern analysis systems to outperform manual image interpretation. In fact, recent work in pattern recognition has demonstrated that computers can equal or even surpass image classification and pattern analysis by human experts. We have previously 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 (see AG000671-13) 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 X-Rays scored as free of OA by a panel of three radiologists. Subsequently, we were able to further characterize OA progression and identify an early, slow period of change followed by rapid degeneration. We are following up our knee X-ray studies with an MRI dataset obtained from the Osteoarthritis Initiative as well as experimental MRI samples imaged here at NIA through a collaboration with Dr. Richard Spencer (NIA/LCI). In a recent study, we developed a technique using multivariate linear regression of image features derived from several types of MRI scans to construct a continuously variable cartilage quality score similar to an OARSI grade. The OARSI grade is determined histologically, and involves an invasive procedure that is not amenable to early screening or tracking disease progress. While MRI methods are non-invasive, they must first be correlated with histological grading schemes before they can be used in diagnosis or evaluating cartilage quality. We analyzed samples from cadavers that were imaged using several MRI modalities as well as scored histologically using the OARSI scale. Standard classification experiments using single MRI scans of degraded vs. non-degraded cartilage as determined by OARSI had accuracies >80%, which are substantially higher than published reports correlating scores derived from MRI images with OARSI grades. Our multivariate regression of image features from multimodal MRI scans produced a continuous score that was well correlated with the OARSI grade of the same samples (r >0.65, p <10-5). Defining a continuous grading system based on a non-invasive procedure is a key element in evaluating osteoarthritis treatment strategies. 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. Current work on studying physiological age in humans uses imaging available for several tissues 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 (see AG000674-10). We are investigating the degree to which physiological age progresses synchronously in different tissues within individuals. Additionally we are using images available through the BLSA together with physiological factors measured in study participants to look for new concurrent and predictive correlations. One of the image sets currently under study are abdominal CT scans. The viscera, bone, subcutaneous and visceral fat in these scans has been segmented into separate image masks by Dr. Sokratis Makrogiannis (NIA/LSS). Our analysis of these image masks indicates that a substantial aging signal is present in visceral fat as well as in the unsegmented whole CT scans. Even though there is generally more subcutaneous fat than visceral fat, it accounts for significantly less age-correlated signal. These results are based on cross-validation of classifiers trained on a middle-age group (56-70) and an older group (81-99). This study is currently being readied for publication. A second application of CT is in the SardNIA project, where there is a collection of peripheral quantitative CT (pQCT) images performed of the calf, leg and thigh on 5,000 participants. These images will be analyzed for image features that correlate with or are predictive of the various physiological traits measured in this study, including their changes associated with aging. Another radiology project is in collaboration with Dr. Maria Knoll at the Johns Hopkins Bloomberg School of Public Health. Here the goal is to diagnose viral vs., bacterial pneumonia in children using chest X-rays. A rapid accurate diagnosis of the nature of this disease will dramatically improve outcomes for children in developing countries. A standard set of chest X-rays is available from the World Health Organization that is well annotated, with each X-ray having been read by multiple expert radiologists forming a solid ground truth for training machine classifiers. The major challenge posed by this set is the extreme variation in the physical size of the subjects due to the variation in their ages. We have developed a strategy to compensate for this size variation by working with a student at JHSPH to manually annotate the X-rays with a set of fiducial marks that are anatomically comparable across the X-rays regardless of subject size. Using these manually aligned regions of the lung, we have preliminary indications that such a diagnosis may be possible. We are currently assembling a completed manually annotated set from the WHO, as well as a different set of X-rays that has recently become available through JHSPH.
| 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 |
