AIMS: To investigate the morphology of the optic disc and retinal vessels in children with surgically treated hydrocephalus. METHODS: A prospective, population-based study was performed in 69 children (median age 9.6 years) with early surgically treated hydrocephalus. All children were examined by ophthalmoscopy. Additionally, optic disc and retinal vessel morphology was evaluated in 55 children by digital image analysis of ocular fundus photographs. RESULTS: Optic atrophy was found in 10 of 69 children (14%). In comparison with a reference group, the median optic-disc area was significantly smaller (p = 0.013) in the children with hydrocephalus. There was no corresponding difference in cup area, so the rim area was significantly smaller in the hydrocephalic children (p = 0.002). Children with hydrocephalus had an abnormal retinal vascular pattern, with significantly straighter retinal arteries and fewer central vessel branching points compared with controls (p
Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.
Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I2 statistics.
BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD-risk association (1.51 (1.41, 1.61); I2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV-risk association (1.44 (1.34, 1.54); I2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I2 = 0%, P = 0.36, respectively).
When volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.
Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years). Furthermore, we provide validation data from a regional segmentation pipeline to assure the integrity of the dataset.
Cites: Neuroimage. 2010 Feb 1;49(3):2352-65 PMID 19857578
To establish the agreement between image grading of conventional (45°) and ultra wide-angle (200°) digital images in the macula.
In 2008, the 12-year follow-up was conducted on 573 participants of the Reykjavik Eye Study. This study included the use of the Optos P200C AF ultra wide-angle laser scanning ophthalmoscope alongside Zeiss FF 450 conventional digital fundus camera on 121 eyes with or without age-related macular degeneration using the International Classification System. Of these eyes, detailed grading was carried out on five cases each with hard drusen, geographic atrophy and chorioretinal neovascularisation, and six cases of soft drusen. Exact agreement and ?-statistics were calculated.
Comparison of the conventional and ultra wide-angle images in the macula showed an overall 96.43% agreement (?=0.93) with no disagreement at end-stage disease; although in one eye chorioretinal neovascularisation was graded as drusenoid pigment epithelial detachment. Of patients with drusen only, the exact agreement was 96.1%. The detailed grading showed no clinically significant disagreement between the conventional 45° and 200° images.
On the basis of our results, there is a good agreement between grading conventional and ultra wide-angle images in the macula.
Breakthrough Breast Cancer Research Unit, School of Cancer, Enabling Sciences and Technology, University of Manchester, Manchester Academic Health Science Centre, Paterson Institute for Cancer Research, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, M20 4BX, UK.
The emergence of automated image analysis algorithms has aided the enumeration, quantification, and immunohistochemical analyses of tumor cells in both whole section and tissue microarray samples. To date, the focus of such algorithms in the breast cancer setting has been on traditional markers in the common invasive ductal carcinoma subtype. Here, we aimed to optimize and validate an automated analysis of the cell cycle regulator cyclin D1 in a large collection of invasive lobular carcinoma and relate its expression to clinicopathologic data. The image analysis algorithm was trained to optimally match manual scoring of cyclin D1 protein expression in a subset of invasive lobular carcinoma tissue microarray cores. The algorithm was capable of distinguishing cyclin D1-positive cells and illustrated high correlation with traditional manual scoring (?=0.63). It was then applied to our entire cohort of 483 patients, with subsequent statistical comparisons to clinical data. We found no correlation between cyclin D1 expression and tumor size, grade, and lymph node status. However, overexpression of the protein was associated with reduced recurrence-free survival (P=.029), as was positive nodal status (P
OBJECTIVE: To determine the role of cell proliferation neoplastic progression in the larynx and possibly derive criteria of clinical significance using automated quantitative image analysis. MATERIAL AND METHODS: In a retrospective study involving archival material, the occurrence and location, size, shape and staining intensity of proliferating cell nuclear antigen (PCNA)-positive cells (12,538 cells in total) were analyzed in squamous cell carcinoma (SCC), as well as in pre- and non-neoplastic conditions, using computer-assisted morphometry with reproducibility and sensitivity exceeding 99%. RESULTS: Immunohistochemically detectable PCNA-positive cells were located in the basal layer in non-neoplastic states, in well-differentiated SCCs in layers adjacent to the basal membrane and in poorly differentiated neoplasms in the neoplastic epithelial islets. An increased degree of dysplasia was associated with an increased number of PCNA-immunoreactive cells of increased nuclear size and staining intensity. There was a significant difference between carcinomas and dysplasia in terms of altered nuclear shape. With increasing malignancy of SCCs, nuclear shape alterations and PCNA staining intensity increased, whilst nuclear size decreased. CONCLUSIONS: Automated image analysis of cell populations allowed the identification of populations of malignant cells and provided information on the severity of preneoplastic and neoplastic conditions of use in studies of tumor behavior and with potential clinical application.
In this study, a fully automated texture-based segmentation and recognition system for lesion and lungs from CT of thorax is presented. For the segmentation part, we have extracted texture features by Gabor filtering the images, and, then combined these features to segment the target volume by using Fuzzy C Means (FCM) clustering. Since clustering is sensitive to initialization of cluster prototypes, optimal initialization of the cluster prototypes was done by using a Genetic Algorithm. For the recognition stage, we have used cortex like mechanism for extracting statistical features in addition to shape-based features. The segmented regions showed a high degree of imbalance between positive and negative samples, so we employed over and under sampling for balancing the data. Finally, the balanced and normalized data was subjected to Support Vector Machine (SimpleSVM) for training and testing. Results reveal an accuracy of delineation to be 94.06%, 94.32% and 89.04% for left lung, right lung and lesion, respectively. Average sensitivity of the SVM classifier was seen to be 89.48%.
OBJECTIVE: The purpose of this study was to compare volumetric image processing techniques for reducing noise and speckle while retaining tissue structures in 3-dimensional (3D) gray scale ultrasound imaging. METHODS: Eighty subjects underwent a clinically indicated abdominal or obstetric 3D ultrasound examination (20 hepatic, 20 renal, and 40 obstetric cases). Volume data were processed on a pixel ("2-dimensional [2D] processing") or a voxel ("3D processing") basis using commercially available image enhancement software (ContextVision AB, Linköping, Sweden). Randomized, side-by-side comparisons of the image processing techniques were performed for each subject. An independent and blinded reader scored the volumes for image quality on a 3-point scale from 1 (worst) to 3 (best) and compared the results using a nonparametric Wilcoxson signed rank test. RESULTS: The 40 subjects with abdominal 3D imaging received a mean score (+/- 1 SD) of 1.52 +/- 0.51, 2.45 +/- 0.60, and 2.75 +/- 0.44 for the original, the 2D processed, and the 3D processed volumes, respectively. The differences between the unprocessed and the processed volumes were highly statistically significant (P
Functional MR imaging (fMRI) has been used in detecting neuronal activation and intrinsic blood flow fluctuations in the brain cortex. This article is aimed for comparing the methods for analyzing the nondeterministic flow fluctuations. Fast Fourier Transformation (FFT), cross correlation (CC), spatial principal component analysis (sPCA), and independent component analysis (sICA) were compared. 15 subjects were imaged at 1.5 T. Three quantitative measures were compared: (1) The number of subjects with identifiable fluctuation, (2) the volume, and (3) mean correlation coefficient (MCC) of the detected voxels. The focusing on cortical structures and the overall usability were qualitatively assessed. sICA was spatially most accurate but time consuming, robust, and detected voxels with high temporal synchrony. The CC and FFT were fast suiting primary screening. The CC detected highest temporal synchrony but the subjective detection for reference vector produced excess variance of the detected volumes. The FFT and sPCA were not spatially accurate and did not detect adequate temporal synchrony of the voxels.
RATIONALE AND OBJECTIVES: To introduce and evaluate a novel, image fusion-based technique that can be used to compare the findings of primary and control brain magnetic resonance imaging scans, with special attention to the differences found in this comparison. MATERIALS AND METHODS: A new technique named "colored difference mapping" was applied to the brain examinations of five patients. The possible changes in the magnetic resonance imaging findings were analyzed by the colored difference mapping technique and by using conventional film reading and the results were compared. RESULTS: Colored difference mapping accurately depicts the differences between successive magnetic resonance images and reveals small changes that are difficult to perceive in a visual evaluation. CONCLUSION: Colored difference mapping is suitable for comparison of images between two different radiologic examinations and helps to show even minimal changes in brain tissues.