CellAging: a tool to study segregation and partitioning in division in cell lineages of Escherichia coli.

MOTIVATION: Cell division in Escherichia coli is morphologically symmetric. However, as unwanted protein aggregates are segregated to the cell poles and, after divisions, accumulate at older poles, generate asymmetries in sister cells' vitality. Novel single-molecule detection techniques allow observing aging-related processes in vivo, over multiple generations, informing on the underlying mechanisms. RESULTS: CellAging is a tool to automatically extract information on polar segregation and partitioning in division of aggregates in E.coli, and on cellular vitality. From time-lapse, parallel brightfield and fluorescence microscopy images, it performs cell segmentation, alignment of brightfield and fluorescence images, lineage construction and pole age determination, and it computes aging-related features. We exemplify its use by analyzing spatial distributions of fluorescent protein aggregates from images of cells across generations. AVAILABILITY: CellAging, instructions and an example are available at http://www.cs.tut.fi/%7esanchesr/cellaging/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Monitoring of drusen and geographic atrophy area size after cataract surgery using the MD3RI tool for computer-aided contour drawing.

BACKGROUND/AIMS: To monitor possible changes in the cumulated drusen or geographic atrophy area size (CDGAS) of nonexudative age-related macular degeneration (AMD) in patients before and after cataract surgery, using a new tool for computer-aided image quantification. METHODS: Randomized, prospective, clinical trial. 54 patients with cataract and nonexudative AMD were randomly assigned into an early surgery group (ES = 28) and a control group (CO = 26) with a 6-month delay of surgery. CDGAS was determined with the MD3RI tool for contour drawing in a central region of digitized fundus photographs, measuring 3,000 µm in diameter. To evaluate CDGAS progression, differences in pixels and square millimeters were calculated by equivalent tests. RESULTS: Forty-nine patients completed the visits over the 12-month period (ES = 27 and CO = 22). Mean pixel values increased from 201.5 (11.33 × 10(-3) mm(2)) to 202.7 (11.39 × 10(-3) mm(2)) in the ES group and from 191.6 (10.77 × 10(-3) mm(2)) to 194.6 (10.94 × 10(-3) mm(2)) in the CO group. Finally, equivalence of CDGAS differences between ES and CO could be demonstrated. No exudative AMD was recorded during the study period. CONCLUSION: In our cohorts, no significant changes were found in CDGAS 12 months after cataract surgery. The MD3RI software could serve as an efficient, precise and objective tool for AMD quantification and monitoring in future trials.

Automated drusen detection in retinal images using analytical modelling algorithms.

BACKGROUND: Drusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD). They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reproduce when performed manually. METHODS: This article proposes a methodology for Automatic Drusen Deposits Detection and quantification in Retinal Images (AD3RI) by using digital image processing techniques. It includes an image pre-processing method to correct the uneven illumination and to normalize the intensity contrast with smoothing splines. The drusen detection uses a gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. The detected drusen are then fitted by Modified Gaussian functions, producing a model of the image that is used to evaluate the affected area.Twenty two images were graded by eight experts, with the aid of a custom made software and compared with AD3RI. This comparison was based both on the total area and on the pixel-to-pixel analysis. The coefficient of variation, the intraclass correlation coefficient, the sensitivity, the specificity and the kappa coefficient were calculated. RESULTS: The ground truth used in this study was the experts' average grading. In order to evaluate the proposed methodology three indicators were defined: AD3RI compared to the ground truth (A2G); each expert compared to the other experts (E2E) and a standard Global Threshold method compared to the ground truth (T2G).The results obtained for the three indicators, A2G, E2E and T2G, were: coefficient of variation 28.8 %, 22.5 % and 41.1 %, intraclass correlation coefficient 0.92, 0.88 and 0.67, sensitivity 0.68, 0.67 and 0.74, specificity 0.96, 0.97 and 0.94, and kappa coefficient 0.58, 0.60 and 0.49, respectively. CONCLUSIONS: The gradings produced by AD3RI obtained an agreement with the ground truth similar to the experts (with a higher reproducibility) and significantly better than the Threshold Method. Despite the higher sensitivity of the Threshold method, explained by its over segmentation bias, it has lower specificity and lower kappa coefficient. Therefore, it can be concluded that AD3RI accurately quantifies drusen, using a reproducible method with benefits for ARMD evaluation and follow-up.