Sila Kurugol

MUC5B Promoter Polymorphism and Interstitial Lung Abnormalities

BACKGROUND A common promoter polymorphism (rs35705950) in MUC5B, the gene encoding mucin 5B, is associated with idiopathic pulmonary fibrosis. It is not known whether this polymorphism is associated with interstitial lung disease in the general population. METHODS We performed a blinded assessment of interstitial lung abnormalities detected in 2633 participants in the Framingham Heart Study by means of volumetric chest computed tomography (CT). We evaluated the relationship between the abnormalities and the genotype at the rs35705950 locus. RESULTS Of the 2633 chest CT scans that were evaluated, interstitial lung abnormalities were present in 177 (7%). Participants with such abnormalities were more likely to have shortness of breath and chronic cough and reduced measures of total lung and diffusion capacity, as compared with participants without such abnormalities. After adjustment for covariates, for each copy of the minor rs35705950 allele, the odds of interstitial lung abnormalities were 2.8 times greater (95% confidence interval [CI], 2.0 to 3.9; P<0.001), and the odds of definite CT evidence of pulmonary fibrosis were 6.3 times greater (95% CI, 3.1 to 12.7; P<0.001). Although the evidence of an association between the MUC5B genotype and interstitial lung abnormalities was greater among participants who were older than 50 years of age, a history of cigarette smoking did not appear to influence the association. CONCLUSIONS The MUC5B promoter polymorphism was found to be associated with interstitial lung disease in the general population. Although this association was more apparent in older persons, it did not appear to be influenced by cigarette smoking. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT00005121.).

Pilot study of semiautomated localization of the dermal/epidermal junction in reflectance confocal microscopy images of skin

Reflectance confocal microscopy (RCM) continues to be translated toward the detection of skin cancers in vivo. Automated image analysis may help clinicians and accelerate clinical acceptance of RCM. For screening and diagnosis of cancer, the dermal/epidermal junction (DEJ), at which melanomas and basal cell carcinomas originate, is an important feature in skin. In RCM images, the DEJ is marked by optically subtle changes and features and is difficult to detect purely by visual examination. Challenges for automation of DEJ detection include heterogeneity of skin tissue, high inter-, intra-subject variability, and low optical contrast. To cope with these challenges, we propose a semiautomated hybrid sequence segmentation/classification algorithm that partitions z-stacks of tiles into homogeneous segments by fitting a model of skin layer dynamics and then classifies tile segments as epidermis, dermis, or transitional DEJ region using texture features. We evaluate two different training scenarios: 1. training and testing on portions of the same stack; 2. training on one labeled stack and testing on one from a different subject with similar skin type. Initial results demonstrate the detectability of the DEJ in both scenarios with epidermis/dermis misclassification rates smaller than 10% and average distance from the expert labeled boundaries around 8.5 μm.

Automated Delineation of Dermal–Epidermal Junction in Reflectance Confocal Microscopy Image Stacks of Human Skin

Reflectance confocal microscopy (RCM) images skin noninvasively, with optical sectioning and nuclear-level resolution comparable with that of pathology. On the basis of the assessment of the dermal-epidermal junction (DEJ) and morphologic features in its vicinity, skin cancer can be diagnosed in vivo with high sensitivity and specificity. However, the current visual, qualitative approach for reading images leads to subjective variability in diagnosis. We hypothesize that machine learning-based algorithms may enable a more quantitative, objective approach. Testing and validation were performed with two algorithms that can automatically delineate the DEJ in RCM stacks of normal human skin. The test set was composed of 15 fair- and 15 dark-skin stacks (30 subjects) with expert labelings. In dark skin, in which the contrast is high owing to melanin, the algorithm produced an average error of 7.9±6.4 μm. In fair skin, the algorithm delineated the DEJ as a transition zone, with average error of 8.3±5.8 μm for the epidermis-to-transition zone boundary and 7.6±5.6 μm for the transition zone-to-dermis. Our results suggest that automated algorithms may quantitatively guide the delineation of the DEJ, to assist in objective reading of RCM images. Further development of such algorithms may guide assessment of abnormal morphological features at the DEJ.

Aorta segmentation with a 3D level set approach and quantification of aortic calcifications in non-contrast chest CT

Automatic aorta segmentation in thoracic computed tomography (CT) scans is important for aortic calcification quantification and to guide the segmentation of other central vessels. We propose an aorta segmentation algorithm consisting of an initial boundary detection step followed by 3D level set segmentation for refinement. Our algorithm exploits aortic cross-sectional circularity: we first detect aorta boundaries with a circular Hough transform on axial slices to detect ascending and descending aorta regions, and we apply the Hough transform on oblique slices to detect the aortic arch. The centers and radii of circles detected by Hough transform are fitted to smooth cubic spline functions using least-squares fitting. From these center and radius spline functions, we reconstruct an initial aorta surface using the Frenet frame. This reconstructed tubular surface is further refined with 3D level set evolutions. The level set framework we employ optimizes a functional that depends on both edge strength and smoothness terms and evolves the surface to the position of nearby edge location corresponding to the aorta wall. After aorta segmentation, we first detect the aortic calcifications with thresholding applied to the segmented aorta region. We then filter out the false positive regions due to nearby high intensity structures. We tested the algorithm on 45 CT scans and obtained a closest point mean error of 0.52 ± 0.10 mm between the manually and automatically segmented surfaces. The true positive detection rate of calcification algorithm was 0.96 over all CT scans.

Motion Compensated Abdominal Diffusion Weighted MRI by Simultaneous Image Registration and Model Estimation (SIR-ME).

Non-invasive characterization of water molecules mobility variations by quantitative analysis of diffusion-weighted MRI (DW-MRI) signal decay in the abdomen has the potential to serve as a biomarker in gastrointestinal and oncological applications. Accurate and reproducible estimation of the signal decay model parameters is challenging due to the presence of respiratory, cardiac, and peristalsis motion. Independent registration of each b-value image to the b-value=0 s/mm(2) image prior to parameter estimation might be sub-optimal because of the low SNR and contrast difference between images of varying b-value. In this work, we introduce a motion-compensated parameter estimation framework that simultaneously solves image registration and model estimation (SIR-ME) problems by utilizing the interdependence of acquired volumes along the diffusion weighting dimension. We evaluated the improvement in model parameters estimation accuracy using 16 in-vivo DW-MRI data sets of Crohns disease patients by comparing parameter estimates obtained using the SIR-ME model to the parameter estimates obtained by fitting the signal decay model to the acquired DW-MRI images. The proposed SIR-ME model reduced the average root-mean-square error between the observed signal and the fitted model by more than 50%. Moreover, the SIR-ME model estimates discriminate between normal and abnormal bowel loops better than the standard parameter estimates.

Abdominal Visceral Adipose Tissue is Associated with Myocardial Infarction in Patients with COPD

BACKGROUND Cardiovascular diseases are frequent and a major cause of death in patients with chronic obstructive pulmonary disease (COPD). In the general population, various fat depots including abdominal visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat have been linked to increased risk of cardiovascular diseases. We hypothesize that these adipose tissue compartments are associated with myocardial infarction (MI) in patients with COPD. METHODS We collected measures of VAT and SAT areas and liver attenuation on the computed tomography scan of the chest from 1267 patients with COPD. MI was a self-reported physician-diagnosed outcome. The association between fat depots and self-reported history of MI was assessed by logistic regression analysis in which the patients within the 2 lowest tertiles of VAT and SAT areas were the reference group. RESULTS Eighty three patients (6.6%) reported a history of MI at the time of enrollment. Compared to patients who did not have an MI episode, those who had a prior MI had a higher VAT area (mean ± SD, 303.4 ± 208.5 vs. 226.8 ± 172.6 cm2; P=0.002) with no differences in SAT area and liver fat. After adjustment for age, gender, obesity, pack years of smoking, hypertension, high cholesterol, and diabetes, patients within the upper tertile (vs. those in the lower tertiles) of VAT area had increased odds of MI (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.02 - 3.41). CONCLUSION Increased abdominal visceral fat is independently associated with a history of MI in individuals with COPD.

Childhood-Onset Asthma in Smokers. Association between CT Measures of Airway Size, Lung Function, and Chronic Airflow Obstruction

RATIONALE AND OBJECTIVES Asthma is associated with chronic airflow obstruction. Our goal was to assess the association of computed tomographic measures of airway wall volume and lumen volume with the FEV1 and chronic airflow obstruction in smokers with childhood-onset asthma. METHODS We analyzed clinical, lung function, and volumetric computed tomographic airway volume data from 7,266 smokers, including 590 with childhood-onset asthma. Small wall volume and small lumen volume of segmental airways were defined as measures 1 SD below the mean. We assessed the association between small wall volume, small lumen volume, FEV1, and chronic airflow obstruction (post-bronchodilator FEV1/FVC ratio < 0.7) using linear and logistic models. MEASUREMENTS AND MAIN RESULTS Compared with subjects without childhood-onset asthma, those with childhood-onset asthma had smaller wall volume and lumen volume (P < 0.0001) of segmental airways. Among subjects with childhood-onset asthma, those with the smallest wall volume and lumen volume had the lowest FEV1 and greatest odds of chronic airflow obstruction. A similar tendency was seen in those without childhood-onset asthma. When comparing these two groups, both small wall volume and small lumen volume were more strongly associated with FEV1 and chronic airflow obstruction among subjects with childhood-asthma in multivariate models. CONCLUSION In smokers with childhood-onset asthma, smaller airways are associated with reduced lung function and chronic airflow obstruction. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images

For radiotherapy planning, contouring of target volume and healthy structures at risk in CT volumes is essential. To automate this process, one of the available segmentation techniques can be used for many thoracic organs except the esophagus, which is very hard to segment due to low contrast. In this work we propose to initialize our previously introduced model based 3D level set esophagus segmentation method with a principal curve tracing (PCT) algorithm, which we adapted to solve the esophagus centerline detection problem. To address challenges due to low intensity contrast, we enhanced the PCT algorithm by learning spatial and intensity priors from a small set of annotated CT volumes. To locate the esophageal wall, the model based 3D level set algorithm including a shape model that represents the variance of esophagus wall around the estimated centerline is utilized. Our results show improvement in esophagus segmentation when initialized by PCT compared to our previous work, where an ad hoc centerline initialization was performed. Unlike previous approaches, this work does not need a very large set of annotated training images and has similar performance.

Detection of the dermis/epidermis boundary in reflectance confocal images using multi-scale classifier with adaptive texture features

Reflectance confocal microscopy is an emerging modality for dermatology applications, especially in-situ and bedside detection of skin cancers. Work to date has concentrated on hardware development and validation by clinicians in comparison with standard histological staining. As this technology gains acceptance, the development of automated processing methods becomes more important. We concentrate here on detection of the dominant internal feature of the skin, the epidermis/dermis boundary, a complex corrugated 3-dimensional layer marked by optically subtle changes and features. We adopt a machine learning approach to this segmentation problem, using a hierarchical multi-scale classifier with sophisticated on-line feature selection, to minimize the required expert labeling and maximize the range of potential features in the face of high inter- and intra-subject variability and low optical contrast. Initial results indicate the ability of our approach to recover the complex 3-D boundary surface.

Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions.

PURPOSE The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease. METHODS The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearby edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width. RESULTS The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging. CONCLUSIONS The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.