Revisiting natural user interaction in virtual world

Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers. The virtual environment is operated on both Google Cardboard and HoloLens, the two popular wireless head-mounted displays. Evaluation results reveal the preferences of users over different natural user interaction methods.

AnatomySketch: An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development

The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human-computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software .

Touchscreen games to detect cognitive impairment in senior adults. A user-interaction pilot study

INTRODUCTION

Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. Today, 47 million people live with dementia worldwide. This number is projected to increase to more than 131 million by 2050, as populations age. Therefore, the World Health Organization considers serious cognitive deterioration a public health priority.

OBJECTIVES

Advanced cognitive evaluation mechanisms are needed to help make an early diagnosis. These new mechanisms should overcome the limitations of current neuropsychological tests, including delayed detection; being perceived as intrusive; being non-ecological; being dependent on confounding factors; or their administration being expensive, among others. A promising novel approach consists of the introduction of serious games based on virtual reality and machine learning able to assess cognitive traits relevant to the diagnosis of mild cognitive impairment and Alzheimer's disease.

METHODS

As a result of a preliminary pilot experiment, promising evidence was obtained about the predictive power of this solution. However, for these new serious games to be effective, evidence has to be gathered on the player experience by senior adults, avoiding the limitations of traditional tests at the same time. This study addresses these aspects with the participation of 74 senior users and 15 test administrators.

RESULTS

Main findings confirm the usability and playability of Panoramix, a game battery designed according to the principles discussed above, its technological acceptability and its accessibility. For example, in relation to acceptability, the game battery was scored 4.39 in a 5-point scale, while its average usability score was 4.45 regardless of socio-cultural level or previous experience with digital technologies. In addition, health professionals confirm both, usability and playability, levels with an average score of 6.5 in a 7-point scale. Participants' willingness of using this kind of systems for cognitive evaluation was also confirmed.

CONCLUSION

Promising results obtained pave the way for additional work to confirm the diagnostic validity according to clinical standards of these new cognitive assessment tools.

Augmentation of the think aloud method with users' perspectives for the selection of a picture archiving and communication system

OBJECTIVES

Users attitude toward a picture archiving and communication system (PACS) and their interaction with this system are among the most important factors that influence its acceptance. This study aimed to augment the user's interaction with the user's perspective to select a usable PACS among three systems available on the market.

METHODS

We augmented the think aloud (TA) usability evaluation method with the Post-Study System Usability Questionnaire (PSSUQ) to compare user interaction problems of three PACS user interfaces. Four radiologists and four internist physicians participated in this study. Usability characteristics including efficiency, effectiveness, learnability, error, and satisfaction were used to assess the usability of each PACS.

RESULTS

There was a significant difference in efficiency (p = 0.01), effectiveness (p = 0.005), learnability (p = 0.001), and satisfaction (p = 0.009). However, no significant difference in the number of errors (p = 0.18), mouse clicks and keystrokes (p = 0.12), and the number of usability problems (p = 0.6) were observed among the three PACS systems studied.

CONCLUSIONS

This study showed that applying the proposed approach to augment TA with the user's perspective addresses almost all of the theoretical aspects of usability and can be employed to select the most usable PACS.

A Survey of Authentication Schemes in Telecare Medicine Information Systems

E-Healthcare is an emerging field that provides mobility to its users. The protected health information of the users are stored at a remote server (Telecare Medical Information System) and can be accessed by the users at anytime. Many authentication protocols have been proposed to ensure the secure authenticated access to the Telecare Medical Information System. These protocols are designed to provide certain properties such as: anonymity, untraceability, unlinkability, privacy, confidentiality, availability and integrity. They also aim to build a key exchange mechanism, which provides security against some attacks such as: identity theft, password guessing, denial of service, impersonation and insider attacks. This paper reviews these proposed authentication protocols and discusses their strengths and weaknesses in terms of ensured security and privacy properties, and computation cost. The schemes are divided in three broad categories of one-factor, two-factor and three-factor authentication schemes. Inter-category and intra-category comparison has been performed for these schemes and based on the derived results we propose future directions and recommendations that can be very helpful to the researchers who work on the design and implementation of authentication protocols.

An efficient conditional random field approach for automatic and interactive neuron segmentation

We present a new graphical-model-based method for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. For automated reconstruction, our learning based model selects a collection of nodes from a hierarchical merging tree as the proposed segmentation. More specifically, this is achieved by training a conditional random field (CRF) whose underlying graph is the watershed merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our results are comparable to the results of state-of-the-art methods. Furthermore, both the inference and the training are very efficient as the graph is tree-structured. The problem of neuron segmentation requires extremely high segmentation quality. Therefore, proofreading, namely, interactively correcting mistakes of the automatic method, is a necessary module in the pipeline. Based on our efficient tree-structured inference algorithm, we develop an interactive segmentation framework which only selects locations where the model is uncertain for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Only giving a limited number of choices makes the user interaction very efficient. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.