Inexpensive Monocular Pico-Projector-based Augmented Reality Display for Surgical Microscope

Abstrac

Comparative Evaluation of Monocular Augmented-Reality Display for Surgical Microscopes

Abstract-Medical augmented reality has undergone much development recently. However, there is a lack of studies quantitatively comparing the different display options available. This paper compares the effects of different graphical overlay systems in a simple micromanipulation task with "soft" visual servoing. We compared positioning accuracy in a real-time visually-guided task using Micron, an active handheld tremor-canceling microsurgical instrument, using three different displays: 2D screen, 3D screen, and microscope with monocular image injection. Tested with novices and an experienced vitreoretinal surgeon, display of virtual cues in the microscope via an augmented reality injection system significantly decreased 3D error (p < 0.05) compared to the 2D and 3D monitors when confounding factors such as magnification level were normalized

In vivo imaging of middle-ear and inner-ear microstructures of a mouse guided by SD-OCT combined with a surgical microscope

We developed an augmented-reality system that combines optical coherence tomography (OCT) with a surgical microscope. By sharing the common optical path in the microscope and OCT, we could simultaneously acquire OCT and microscope views. The system was tested to identify the middle-ear and inner-ear microstructures of a mouse. Considering the probability of clinical application including otorhinolaryngology, diseases such as middle-ear effusion were visualized using in vivo mouse and OCT images simultaneously acquired through the eyepiece of the surgical microscope during surgical manipulation using the proposed system. This system is expected to realize a new practical area of OCT application.open0

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

On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

Online Semantic Labeling of Deformable Tissues for Medical Applications

University of Minnesota Ph.D. dissertation. May 2017. Major: Mechanical Engineering. Advisor: Timothy Kowalewski. 1 computer file (PDF); ix, 133 pages.Surgery remains dangerous, and accurate knowledge of what is presented to the surgeon can be of great importance. One technique to automate this problem is non-rigid tracking of time-of-flight camera scans. This requires accurate sensors and prior information as well as an accurate non-rigid tracking algorithm. This thesis presents an evaluation of four algorithms for tracking and semantic labeling of deformable tissues for medical applications, as well as additional studies on a stretchable flexible smart skin and dynamic 3D bioprinting. The algorithms were developed and tested for this study, and were evaluated in terms of speed and accuracy. The algorithms tested were affine iterative closest point, nested iterative closest point, affine fast point feature histograms, and nested fast point feature histograms. The algorithms were tested against simulated data as well as direct scans. The nested iterative closest point algorithm provided the best balance of speed and accuracy while providing semantic labeling in both simulation as well as using directly scanned data. This shows that fast point feature histograms are not suitable for nonrigid tracking of geometric feature poor human tissues. Secondary experiments were also performed to show that the graphics processing unit provides enough speed to perform iterative closest point algorithms in real-time and that time of flight depth sensing works through an endoscope. Additional research was conducted on related topics, leading to the development of a novel stretchable flexible smart skin sensor and an active 3D bioprinting system for moving human anatomy

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

The life and work of the Revd. T.H.E.C. Espin perpetual curate of tow law with special reference to his astronomical research

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