Age-related changes of the retinal microvasculature.

PURPOSE: Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure. RESULTS: We used two approaches. In the first approach, we segmented the retinal vasculature from fundus photographs and then we correlated 25 parameterized aspects ("traits")-comprising 15 measures of tortuosity, 7 fractal ranges of self-similarity, and 3 measures of junction numbers-with participant age and blood pressure. In the second approach, we examined entire fundus photographs with a set of algorithmic CHARM features. We studied 2,280 Sardinians, ages 20-28, and an U.S. based population from the AREDS study in 1,178 participants, ages 59-84. Three traits (relating to tortuosity, vessel bifurcation number, and vessel endpoint number) showed significant changes with age in both cohorts, and one additional trait (relating to fractal number) showed a correlation in the Sardinian cohort only. When using second approach, we found significant correlations of particular CHARM features with age and blood pressure, which were stronger than those detected when using parameterized traits, reflecting a greater signal from the entire photographs than was captured in the segmented microvasculature. CONCLUSIONS: These findings demonstrate that automated quantitative image analysis of fundus images can reveal general measures of patient health status.

Transcriptome States Reflect Imaging of Aging States.

In this study, we describe a morphological biomarker that detects multiple discrete subpopulations (or "age-states") at several chronological ages in a population of nematodes (Caenorhabditis elegans). We determined the frequencies of three healthy adult states and the timing of the transitions between them across the lifespan. We used short-lived and long-lived strains to confirm the general applicability of the state classifier and to monitor state progression. This exploration revealed healthy and unhealthy states, the former being favored in long-lived strains and the latter showing delayed onset. Short-lived strains rapidly transitioned through the putative healthy state. We previously found that age-matched animals in different age-states have distinct transcriptome profiles. We isolated animals at the beginning and end of each identified state and performed microarray analysis (principal component analysis, relative sample to sample distance measurements, and gene set enrichment analysis). In some comparisons, chronologically identical individuals were farther apart than morphologically identical individuals isolated on different days. The age-state biomarker allowed assessment of aging in a novel manner, complementary to chronological age progression. We found hsp70 and some small heat shock protein genes are expressed later in adulthood, consistent with the proteostasis collapse model.

Differential Aging Signals in Abdominal CT Scans.

RATIONALE AND OBJECTIVES: Changes in the composition of body tissues are major aging phenotypes, but they have been difficult to study in depth. Here we describe age-related change in abdominal tissues observable in computed tomography (CT) scans. We used pattern recognition and machine learning to detect and quantify these changes in a model-agnostic fashion. MATERIALS AND METHODS: CT scans of abdominal L4 sections were obtained from Baltimore Longitudinal Study of Aging (BLSA) participants. Age-related change in the constituent tissues were determined by training machine classifiers to differentiate age groups within male and female strata ("Younger" at 50-70 years old vs "Older" at 80-99 years old). The accuracy achieved by the classifiers in differentiating the age cohorts was used as a surrogate measure of the aging signal in the different tissues. RESULTS: The highest accuracy for discriminating age differences was 0.76 and 0.72 for males and females, respectively. The classification accuracy was 0.79 and 0.71 for adipose tissue, 0.70 and 0.68 for soft tissue, and 0.65 and 0.64 for bone. CONCLUSIONS: Using image data from a large sample of well-characterized pool of participants dispersed over a wide age range, we explored age-related differences in gross morphology and texture of abdominal tissues. This technology is advantageous for tracking effects of biological aging and predicting adverse outcomes when compared to the traditional use of specific molecular biomarkers. Application of pattern recognition and machine learning as a tool for analyzing medical images may provide much needed insight into tissue changes occurring with aging and, further, connect these changes with their metabolic and functional consequences.

Molecular characterization of the transition to mid-life in Caenorhabditis elegans.

We present an initial molecular characterization of a morphological transition between two early aging states. In previous work, an age score reflecting physiological age was developed using a machine classifier trained on images of worm populations at fixed chronological ages throughout their lifespan. The distribution of age scores identified three stable post-developmental states and transitions. The first transition occurs at day 5 post-hatching, where a significant percentage of the population exists in both state I and state II. The temperature dependence of the timing of this transition (Q 10 ~ 1.17) is too low to be explained by a stepwise process with an enzymatic or chemical rate-limiting step, potentially implicating a more complex mechanism. Individual animals at day 5 were sorted into state I and state II groups using the machine classifier and analyzed by microarray expression profiling. Despite being isogenic, grown for the same amount of time, and indistinguishable by eye, these two morphological states were confirmed to be molecularly distinct by hierarchical clustering and principal component analysis of the microarray results. These molecular differences suggest that pharynx morphology reflects the aging state of the whole organism. Our expression profiling yielded a gene set that showed significant overlap with those from three previous age-related studies and identified several genes not previously implicated in aging. A highly represented group of genes unique to this study is involved in targeted ubiquitin-mediated proteolysis, including Skp1-related (SKR), F-box-containing, and BTB motif adaptors.

Improving class separability using extended pixel planes: a comparative study.

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev and wavelets of the gradient transform, as well as Fourier of the Laplacian transform. We demonstrate that all three types of EPP promote class separation. We also explored the effect of EPP on suboptimal feature libraries, using only textural features in one case and only Haralick features in another. The effect of EPP was especially clear for these suboptimal libraries, where the transform-based representations were found to increase separability to a greater extent than scale or subband pyramids. EPP can be particularly useful in new applications where optimal features have not yet been developed.

Automatic detection of melanoma progression by histological analysis of secondary sites.

We present results from machine classification of melanoma biopsies sectioned and stained with hematoxylin/eosin (H&E) on tissue microarrays (TMA). The four stages of melanoma progression were represented by seven tissue types, including benign nevus, primary tumors with radial and vertical growth patterns (stage I) and four secondary metastatic tumors: subcutaneous (stage II), lymph node (stage III), gastrointestinal and soft tissue (stage IV). Our experiment setup comprised 14,208 image samples based on 164 TMA cores. In our experiments, we constructed an HE color space by digitally deconvolving the RGB images into separate H (hematoxylin) and E (eosin) channels. We also compared three different classifiers: Weighted Neighbor Distance (WND), Radial Basis Functions (RBF), and k-Nearest Neighbors (kNN). We found that the HE color space consistently outperformed other color spaces with all three classifiers, while the different classifiers did not have as large of an effect on accuracy. This showed that a more physiologically relevant representation of color can have a larger effect on correct image interpretation than downstream processing steps. We were able to correctly classify individual fields of view with an average of 96% accuracy when randomly splitting the dataset into training and test fields. We also obtained a classification accuracy of 100% when testing entire cores that were not previously used in training (four random trials with one test core for each of 7 classes, 28 tests total). Because each core corresponded to a different patient, this test more closely mimics a clinically relevant setting where new patients are evaluated based on training with previous cases. The analysis method used in this study contains no parameters or adjustments that are specific to melanoma morphology, suggesting it can be used for analyzing other tissues and phenotypes, as well as potentially different image modalities and contrast techniques.

Automatic classification of lymphoma images with transform-based global features.

We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach was employed in this study. At the outer level, raw pixels were transformed with a set of transforms into spectral planes. Simple (Fourier, Chebyshev, and wavelets) and compound transforms (Chebyshev of Fourier and wavelets of Fourier) were computed. Raw pixels and spectral planes were then routed to the second stage (the inner level). At the inner level, the set of multipurpose global features was computed on each spectral plane by the same feature bank. All computed features were fused into a single feature vector. The specimens were stained with hematoxylin (H) and eosin (E) stains. Several color spaces were used: RGB, gray, CIE-L*a*b*, and also the specific stain-attributed H&E space, and experiments on image classification were carried out for these sets. The best signal (98%-99% on earlier unseen images) was found for the HE, H, and E channels of the H&E data set.