Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of the reference standard and the evaluation measures. This framework has been introduced at the MICCAI 2009 workshop 3D Segmentation in the Clinic: A Grand Challenge III, and we compare the results of eight teams that participated. These results show that automated segmentation of the vessel lumen is possible with a precision that is comparable to manual annotation. The framework is open for new submissions through the website http://cls2009.bigr.nl.

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Purpose This paper aims to present a detailed description of Tukuchiy, a framework to dynamically generate adapted user interfaces. Tukuchiy is based on Runa-Kamachiy, a conceptual integration model that combines human–computer interaction (HCI) standards to create user interfaces with user-centered concepts usually addressed by adaptation. Design/methodology/approach The first step was the definition of three profiles: user, context and interface. These profiles contain information, such as user disabilities, location characteristics (e.g. illumination) and preferences (e.g. interface color or type of system help). The next step is to define the rules that ensure usability for different users. All of this information is used to create the Tukuchiy framework, which generates dynamic user interfaces, based on the specified rules. The last step is the validation through a prototype called Idukay. This prototype uses Tukuchiy to provide e-learning services. The functionality and usability of the system was evaluated by five experts. Findings To validate the approach, a prototype of Tukuchiy, called Idukay, was created. Idukay was evaluated by experts in education, computing and HCI, who based their evaluation in the system usability scale (SUS), a standard usability test. According to them, the prototype complies with the usability criteria addressed by Tukuchiy. Research limitations/implications This work was tested in an academic environment and was validated by different experts. Further tests in a production environment are required to fully validate the approach. Originality/value Tukuchiy generates adapted user interfaces based on user and context profiles. Tukuchiy uses HCI standards to ensure usability of interfaces that dynamically change during execution time. The interfaces generated by Tukuchiy adapt to context, functionality, disabilities (e.g. color blindness) and preferences (usage and presentation) of the user. Tukuchiy enforces specific HCI standards for color utilization, button size and grouping, etc., during execution.