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Abstract Background Intra-cellular and inter-cellular protein translocation can be observed by microscopic imaging of tissue sections prepared immunohistochemically. A manual densitometric analysis is time-consuming, subjective and error-prone. An automated quantification is faster, more reproducible, and should yield results comparable to manual evaluation. The automated method presented here was developed on rat liver tissue sections to study the translocation of bile salt transport proteins in hepatocytes. For validation, the cholestatic liver state was compared to the normal biological state. Results An automated quantification method was developed to analyze the translocation of membrane proteins and evaluated in comparison to an established manual method. Firstly, regions of interest (membrane fragments) are identified in confocal microscopy images. Further, densitometric intensity profiles are extracted orthogonally to membrane fragments, following the direction from the plasma membrane to cytoplasm. Finally, several different quantitative descriptors were derived from the densitometric profiles and were compared regarding their statistical significance with respect to the transport protein distribution. Stable performance, robustness and reproducibility were tested using several independent experimental datasets. A fully automated workflow for the information extraction and statistical evaluation has been developed and produces robust results. Conclusions New descriptors for the intensity distribution profiles were found to be more discriminative, i.e. more significant, than those used in previous research publications for the translocation quantification. The slow manual calculation can be substituted by the fast and unbiased automated method.

In this paper, a processing chain is proposed for enhancing larger-scale building structures in magnetometer data using the continuous wavelet transform for extracting the horizontal location, depth, and homogeneity (or shape) of subsurface objects. Even though the values estimated from our data do not clearly describe the archaeological site, they are used as a filter step for removing objects whose depth and homogeneity fall outside a predefined range. This yields a binary map, where valid points represent the horizontal source positions of magnetic anomalies. In a second step, curvilinear subsets of points are identified on a larger scale by the tensor voting framework indicating archaeological relevant structures, such as arcs and lines. Here, a point casts votes on neighbouring points and their values depend on the distance, and relative position of the points. All votes are summed up and the final value discriminates, whether a certain point (and therefore a magnetic source) is part of a salient curve (e.g. arc, line). Thresholding removes points with lower values while keeping those with larger values. Although the result shows many geometric features, it still needs to be combined with other data, e.g. from aerial photography or excavations. In combination, the extracted features continue those structures in the aerial photograph, thus expanding the knowledge about the archaeological site. [ABSTRACT FROM AUTHOR]

The idea of a “common artefact” is a useful metaphor for the design of CSCW systems. Our ACCT model of a common artefact describes structural elements that provide awareness about the work of others. The ACCT model identifies actors, contents, conversations, and tools as the central components of a common artefact, arranged on a shared background. The elements of a common artefact provide both a background visualization of the activity, but also permit dynamic notification of particular events. We explore this process of notification, which is composed of a selection and a presentation stage. We identify the critical factors of the process, in particular we highlight techniques related to temporal and spatial distortion. The framework helps to prepare design decisions of multi-user systems more consciously. [ABSTRACT FROM AUTHOR]

Reports on the use of both ultrasound images and three-dimensional animated heart models as common artifacts to support communication between medical professionals. Augmented-reality communication for diagnostic tasks in cardiology; Overview of the CardiAssist project; Use of an augmented reality scene as a common artefact to support communication between healthcare professionals.

Background:  In times of omnipresent digitization and big data, telemedicine and electronic case files (ECFs) are gaining ground for networking between players in the health care sector. In the context of the SALUS study, this approach is applied in practice in the form of electronic platforms to display and process disease-relevant data of glaucoma patients., Objectives:  The SALUS ECF is designed and implemented to support data acquisition and presentation, monitoring, and outcome control for patients suffering from glaucoma in a clinical setting. Its main aim is to provide a means for out- and inpatient exchange of information between various stakeholders with an intuitive user interface in ophthalmologic care. Instrument data, anamnestic data, and diagnostic assessments need to be accessible and historic data stored for patient monitoring. Quality control of the data is ensured by a reading center., Methods:  Based on an intensive requirement analysis, we implemented the ECF as a web-based application in React with a Datomic back-end exposing REST and GraphQL APIs for data access and import. A flexible role management was developed, which addresses the various tasks of multiple stakeholders in the SALUS study. Data security is ensured by a comprehensive encryption concept. We evaluated the usability and efficiency of the ECF by measuring the durations medical doctors need to enter and work with the data., Results:  The evaluation showed that the ECF is time-saving in comparison to paper-based assessments and offers supportive monitoring and outcome control for numerical and imaging-related data. By allowing patients and physicians to access the digital ECF, data connectivity as well as patient autonomy were enhanced., Conclusion:  ECFs have a great potential to efficiently support all patients and stakeholders involved in the care of glaucoma patients. They benefit from the efficient management and view of the data tailored to their specific role., Competing Interests: None declared., (Thieme. All rights reserved.)

Microscopic images of tissue sections are used for diagnosis and monitoring of therapy, by analysis of protein patterns correlating to disease states. Spatial protein distribution is influenced by protein translocation between different membrane compartments and quantified by comparison of microscopic images of biological samples. Cholestatic liver diseases are characterized by translocation of transport proteins, and quantification of their dislocation offers new diagnostic options. However, reliable and unbiased tools are lacking. The nowadays used manual method is slow, subjective and error-prone. We have developed a new workflow based on automated image analysis and improved it by the introduction of scale-free descriptors for the translocation quantification. This fast and unbiased method can substitute the manual analysis, and the suggested descriptors perform better than the earlier used statistical variance.

Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications of this technique, in particular automated imaging at the cellular level. Due to increasing automation and the availability of integrated instruments, biomedical users are becoming increasingly confronted with the problem of analyzing such data. Image database applications need to combine data management, image analysis and visual data mining. The main point of such a system is a software layer that represents objects within an image and the ability to use a large spectrum of quantitative and symbolic object features. Image analysis needs to be adapted to each particular experiment; therefore, 'end user programming' will be desired to make the technology more widely applicable.

Virtual reality (VR) is one of the latest developments in cardiac three-dimensional (3-D) ultrasound. A VR heart model linked to 3-D echocardiographic image datasets provides the observers spatial information regarding a 3-D image dataset and prevents the "lost in space effect" in difficult and relevant coupled diseases when integrated into 3-D reconstruction software. Standardized echocardiographic views can be selected within the integrated developed VR heart model, and this is the first step to automatic 3-D computations with minimal operator interaction. VR heart models open exciting opportunities in the field of teaching echocardiographic cardiology, diagnosis, and examinable states.