Recent Developments and Future Challenges in Medical Mixed Reality

As AR technology matures, we have seen many applicationsemerge in entertainment, education and training. However, the useof AR is not yet common in medical practice, despite the great po-tential of this technology to help not only learning and training inmedicine, but also in assisting diagnosis and surgical guidance. Inthis paper, we present recent trends in the use of AR across all med-ical specialties and identify challenges that must be overcome tonarrow the gap between academic research and practical use of ARin medicine. A database of 1403 relevant research papers publishedover the last two decades has been reviewed by using a novel re-search trend analysis method based on text mining algorithm. Wesemantically identified 10 topics including varies of technologiesand applications based on the non-biased and in-personal cluster-ing results from the Latent Dirichlet Allocatio (LDA) model andanalysed the trend of each topic from 1995 to 2015. The statisticresults reveal a taxonomy that can best describes the developmentof the medical AR research during the two decades. And the trendanalysis provide a higher level of view of how the taxonomy haschanged and where the focus will goes. Finally, based on the valu-able results, we provide a insightful discussion to the current limi-tations, challenges and future directions in the field. Our objectiveis to aid researchers to focus on the application areas in medicalAR that are most needed, as well as providing medical practitioners with latest technology advancements

3D Human Face Reconstruction and 2D Appearance Synthesis

3D human face reconstruction has been an extensive research for decades due to its wide applications, such as animation, recognition and 3D-driven appearance synthesis. Although commodity depth sensors are widely available in recent years, image based face reconstruction are significantly valuable as images are much easier to access and store. In this dissertation, we first propose three image-based face reconstruction approaches according to different assumption of inputs. In the first approach, face geometry is extracted from multiple key frames of a video sequence with different head poses. The camera should be calibrated under this assumption. As the first approach is limited to videos, we propose the second approach then focus on single image. This approach also improves the geometry by adding fine grains using shading cue. We proposed a novel albedo estimation and linear optimization algorithm in this approach. In the third approach, we further loose the constraint of the input image to arbitrary in the wild images. Our proposed approach can robustly reconstruct high quality model even with extreme expressions and large poses. We then explore the applicability of our face reconstructions on four interesting applications: video face beautification, generating personalized facial blendshape from image sequences, face video stylizing and video face replacement. We demonstrate great potentials of our reconstruction approaches on these real-world applications. In particular, with the recent surge of interests in VR/AR, it is increasingly common to see people wearing head-mounted displays. However, the large occlusion on face is a big obstacle for people to communicate in a face-to-face manner. Our another application is that we explore hardware/software solutions for synthesizing the face image with presence of HMDs. We design two setups (experimental and mobile) which integrate two near IR cameras and one color camera to solve this problem. With our algorithm and prototype, we can achieve photo-realistic results. We further propose a deep neutral network to solve the HMD removal problem considering it as a face inpainting problem. This approach doesn\u27t need special hardware and run in real-time with satisfying results

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Immersive Visualization in Biomedical Computational Fluid Dynamics and Didactic Teaching and Learning

Virtual reality (VR) can stimulate active learning, critical thinking, decision making and improved performance. It requires a medium to show virtual content, which is called a virtual environment (VE). The MARquette Visualization Lab (MARVL) is an example of a VE. Robust processes and workflows that allow for the creation of content for use within MARVL further increases the userbase for this valuable resource. A workflow was created to display biomedical computational fluid dynamics (CFD) and complementary data in a wide range of VE’s. This allows a researcher to study the simulation in its natural three-dimensional (3D) morphology. In addition, it is an exciting way to extract more information from CFD results by taking advantage of improved depth cues, a larger display canvas, custom interactivity, and an immersive approach that surrounds the researcher. The CFD to VR workflow was designed to be basic enough for a novice user. It is also used as a tool to foster collaboration between engineers and clinicians. The workflow aimed to support results from common CFD software packages and across clinical research areas. ParaView, Blender and Unity were used in the workflow to take standard CFD files and process them for viewing in VR. Designated scripts were written to automate the steps implemented in each software package. The workflow was successfully completed across multiple biomedical vessels, scales and applications including: the aorta with application to congenital cardiovascular disease, the Circle of Willis with respect to cerebral aneurysms, and the airway for surgical treatment planning. The workflow was completed by novice users in approximately an hour. Bringing VR further into didactic teaching within academia allows students to be fully immersed in their respective subject matter, thereby increasing the students’ sense of presence, understanding and enthusiasm. MARVL is a space for collaborative learning that also offers an immersive, virtual experience. A workflow was created to view PowerPoint presentations in 3D using MARVL. A resulting Immersive PowerPoint workflow used PowerPoint, Unity and other open-source software packages to display the PowerPoint presentations in 3D. The Immersive PowerPoint workflow can be completed in under thirty minutes

A window to your smartphone: exploring interaction and communication in immersive VR with augmented virtuality

A major drawback of most Head Mounted Displays (HMDs) used in immersive Virtual Reality (VR) is the visual and social isolation of users from their real-world surroundings while wearing these headsets. This partial isolation of users from the real-world might hinder social interactions with friends and family. To address this issue, we present a new method to allow people wearing VR HMDs to use their smartphones without removing their HMDs. To do this, we augment the scene inside the VR HMD with a view of the user's device so that the user can interact with the device without removing the headset. The idea involves the use of additional cameras, such as the Leap Motion device or a high-resolution RGB camera to capture the user's real-world surrounding and augment the virtual world with the content displayed on the smartphone screen. This setup allows VR users to have a window to their smartphone from within the virtual world and a�ord much of the functionality provided by their smartphones, with the potential to reduce undesirable visual and social isolation users may experience when using immersive VR HMDs. This work has been successfully submitted for presentation as a poster in the Computer and Robot Vision Conference 2017 in Edmonton, Alberta, and is scheduled to appear in the conference proceedings at the IEEE Xplore digital library later this year

Multi-touch Detection and Semantic Response on Non-parametric Rear-projection Surfaces

The ability of human beings to physically touch our surroundings has had a profound impact on our daily lives. Young children learn to explore their world by touch; likewise, many simulation and training applications benefit from natural touch interactivity. As a result, modern interfaces supporting touch input are ubiquitous. Typically, such interfaces are implemented on integrated touch-display surfaces with simple geometry that can be mathematically parameterized, such as planar surfaces and spheres; for more complicated non-parametric surfaces, such parameterizations are not available. In this dissertation, we introduce a method for generalizable optical multi-touch detection and semantic response on uninstrumented non-parametric rear-projection surfaces using an infrared-light-based multi-camera multi-projector platform. In this paradigm, touch input allows users to manipulate complex virtual 3D content that is registered to and displayed on a physical 3D object. Detected touches trigger responses with specific semantic meaning in the context of the virtual content, such as animations or audio responses. The broad problem of touch detection and response can be decomposed into three major components: determining if a touch has occurred, determining where a detected touch has occurred, and determining how to respond to a detected touch. Our fundamental contribution is the design and implementation of a relational lookup table architecture that addresses these challenges through the encoding of coordinate relationships among the cameras, the projectors, the physical surface, and the virtual content. Detecting the presence of touch input primarily involves distinguishing between touches (actual contact events) and hovers (near-contact proximity events). We present and evaluate two algorithms for touch detection and localization utilizing the lookup table architecture. One of the algorithms, a bounded plane sweep, is additionally able to estimate hover-surface distances, which we explore for interactions above surfaces. The proposed method is designed to operate with low latency and to be generalizable. We demonstrate touch-based interactions on several physical parametric and non-parametric surfaces, and we evaluate both system accuracy and the accuracy of typical users in touching desired targets on these surfaces. In a formative human-subject study, we examine how touch interactions are used in the context of healthcare and present an exploratory application of this method in patient simulation. A second study highlights the advantages of touch input on content-matched physical surfaces achieved by the proposed approach, such as decreases in induced cognitive load, increases in system usability, and increases in user touch performance. In this experiment, novice users were nearly as accurate when touching targets on a 3D head-shaped surface as when touching targets on a flat surface, and their self-perception of their accuracy was higher

The matrix revisited: A critical assessment of virtual reality technologies for modeling, simulation, and training

A convergence of affordable hardware, current events, and decades of research have advanced virtual reality (VR) from the research lab into the commercial marketplace. Since its inception in the 1960s, and over the next three decades, the technology was portrayed as a rarely used, high-end novelty for special applications. Despite the high cost, applications have expanded into defense, education, manufacturing, and medicine. The promise of VR for entertainment arose in the early 1990\u27s and by 2016 several consumer VR platforms were released. With VR now accessible in the home and the isolationist lifestyle adopted due to the COVID-19 global pandemic, VR is now viewed as a potential tool to enhance remote education. Drawing upon over 17 years of experience across numerous VR applications, this dissertation examines the optimal use of VR technologies in the areas of visualization, simulation, training, education, art, and entertainment. It will be demonstrated that VR is well suited for education and training applications, with modest advantages in simulation. Using this context, the case is made that VR can play a pivotal role in the future of education and training in a globally connected world

Toward Real-Time Video-Enhanced Augmented Reality for Medical Visualization and Simulation

In this work we demonstrate two separate forms of augmented reality environments for use with minimally-invasive surgical techniques. In Chapter 2 it is demonstrated how a video feed from a webcam, which could mimic a laparoscopic or endoscopic camera used during an interventional procedure, can be used to identify the pose of the camera with respect to the viewed scene and augment the video feed with computer-generated information, such as rendering of internal anatomy not visible beyond the image surface, resulting in a simple augmented reality environment. Chapter 3 details our implementation of a similar system to the one previously mentioned, albeit with an external tracking system. Additionally, we discuss the challenges and considerations for expanding this system to support an external tracking system, specifically the Polaris Spectra optical tracker. Because of the relocation of the tracking origin to a point other than the camera center, there is an additional registration step necessary to establish the position of all components within the scene. This modification is expected to increase accuracy and robustness of the system

HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1