Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization

NextMed, Augmented and Virtual Reality platform for 3D medical imaging visualization

The visualization of the radiological results with more advanced techniques than the current ones, such as Augmented Reality and Virtual Reality technologies, represent a great advance for medical professionals, by eliminating their imagination capacity as an indispensable requirement for the understanding of medical images. The problem is that for its application it is necessary to segment the anatomical areas of interest, and this currently involves the intervention of the human being. The Nextmed project is presented as a complete solution that includes DICOM images import, automatic segmentation of certain anatomical structures, 3D mesh generation of the segmented area, visualization engine with Augmented Reality and Virtual Reality, all thanks to different software platforms that have been implemented and detailed, including results obtained from real patients. We will focus on the visualization platform using both Augmented and Virtual Reality technologies to allow medical professionals to work with 3d model representation of medical images in a different way taking advantage of new technologies

DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Spinal involvement in mucopolysaccharidosis IVA (Morquio-Brailsford or Morquio A syndrome): presentation, diagnosis and management.

Mucopolysaccharidosis IVA (MPS IVA), also known as Morquio-Brailsford or Morquio A syndrome, is a lysosomal storage disorder caused by a deficiency of the enzyme N-acetyl-galactosamine-6-sulphate sulphatase (GALNS). MPS IVA is multisystemic but manifests primarily as a progressive skeletal dysplasia. Spinal involvement is a major cause of morbidity and mortality in MPS IVA. Early diagnosis and timely treatment of problems involving the spine are critical in preventing or arresting neurological deterioration and loss of function. This review details the spinal manifestations of MPS IVA and describes the tools used to diagnose and monitor spinal involvement. The relative utility of radiography, computed tomography (CT) and magnetic resonance imaging (MRI) for the evaluation of cervical spine instability, stenosis, and cord compression is discussed. Surgical interventions, anaesthetic considerations, and the use of neurophysiological monitoring during procedures performed under general anaesthesia are reviewed. Recommendations for regular radiological imaging and neurologic assessments are presented, and the need for a more standardized approach for evaluating and managing spinal involvement in MPS IVA is addressed

Health data management in the medical arena

In this paper it is presented an Agency for Integration, Archive and Diffusion of Medical Information (AIDA), which configures a data warehouse, developed using Multi-Agent technology. AIDA is like a symbiont, with a close association with core applications present at any health care facility, such as the Picture Archive Communication System, the Radiological Information System or the Electronic Medical Record Information System. Multi-Agent Systems also configure a new methodology for problem solving

Rapid Development of Medical Imaging Tools with Open-Source Libraries

Rapid prototyping is an important element in researching new imaging analysis techniques and developing custom medical applications. In the last ten years, the open source community and the number of open source libraries and freely available frameworks for biomedical research have grown significantly. What they offer are now considered standards in medical image analysis, computer-aided diagnosis, and medical visualization. A cursory review of the peer-reviewed literature in imaging informatics (indeed, in almost any information technology-dependent scientific discipline) indicates the current reliance on open source libraries to accelerate development and validation of processes and techniques. In this survey paper, we review and compare a few of the most successful open source libraries and frameworks for medical application development. Our dual intentions are to provide evidence that these approaches already constitute a vital and essential part of medical image analysis, diagnosis, and visualization and to motivate the reader to use open source libraries and software for rapid prototyping of medical applications and tools

Augmented Reality in Forensics and Forensic Medicine - Current Status and Future Prospects

Forensic investigations require a vast variety of knowledge and expertise of each specialist involved. With the increase in digitization and advanced technical possibilities, the traditional use of a computer with a screen for visualization and a mouse and keyboard for interactions has limitations, especially when visualizing the content in relation to the real world. Augmented reality (AR) can be used in such instances to support investigators in various tasks at the scene as well as later in the investigation process. In this article, we present current applications of AR in forensics and forensic medicine, the technological basics of AR, and the advantages that AR brings for forensic investigations. Furthermore, we will have a brief look at other fields of application and at future developments of AR in forensics

Exploring individual user differences in the 2D/3D interaction with medical image data

User-centered design is often performed without regard to individual user differences. In this paper, we report results of an empirical study aimed to evaluate whether computer experience and demographic user characteristics would have an effect on the way people interact with the visualized medical data in a 3D virtual environment using 2D and 3D input devices. We analyzed the interaction through performance data, questionnaires and observations. The results suggest that differences in gender, age and game experience have an effect on people’s behavior and task performance, as well as on subjective\ud user preferences