Generic approach for deriving reliability and maintenance requirements through consideration of in-context customer objectives

Not all implementations of reliability are equally effective at providing customer and user benefit. Random system failure with no prior warning or failure accommodation will have an immediate, usually adverse impact on operation. Nevertheless, this approach to reliability, implicit in measurements such as ‘failure rate’ and ‘MTBF’, is widely assumed without consideration of potential benefits of pro-active maintenance. Similarly, it is easy to assume that improved maintainability is always a good thing. However, maintainability is only one option available to reduce cost of ownership and reduce the impact of failure. This paper discusses a process for deriving optimised reliability and maintenance requirements through consideration of in-context customer objectives rather than a product in isolation

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows