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

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2962 ◽  
Author(s):  
Santiago González Izard ◽  
Ramiro Sánchez Torres ◽  
Óscar Alonso Plaza ◽  
Juan Antonio Juanes Méndez ◽  
Francisco José García-Peñalvo
Keyword(s):  
Virtual Reality ◽  
3D Reconstruction ◽  
Medical Image ◽  
New Technologies ◽  
Medical Images ◽  
3D Visualization ◽  
Automatic Segmentation ◽  
Individual Case ◽  
Medical Professionals ◽  
Anatomical Structures

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.

scholarly journals IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

scholarly journals Exploiting Global Structure Information to Improve Medical Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3249
Author(s):  
Jaemoon Hwang ◽  
Sangheum Hwang
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Feature Space ◽  
Medical Image Segmentation ◽  
Global Structure ◽  
Distance Metrics ◽  
Anatomical Structures ◽  
Structure Information ◽  
Anatomical Priors ◽  
Segmentation Models

In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.

3D-Visualization for DICOM Series Based on ITK and VTK

2012 ◽  
Vol 263-266 ◽  
pp. 2530-2533 ◽  
Author(s):  
Hui Dong ◽  
Ling Xia ◽  
Zhi Peng ◽  
Jin Zhang
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
3D Visualization ◽  
Three Dimensional ◽  
Large Data ◽  
Volume Data ◽  
Practical Applications ◽  
Novel Technology ◽  
Speed Up ◽  
Filter Algorithms

Nowadays, medical image processing and three-dimensional visualization have become a very important support for doctor diagnosis and treatment planning. It’s a novel technology that using ITK and VTK to process and display medical images in VC++. In this paper, the Curvature FlowImageFilter class of ITK libraries is used to denoise and smooth the medical images. The MC algorithms is used to reconstruct the volume data in the VS2008. Immediate Mode algorithms and Stripper filter algorithms are adopted to speed up large data processing. The experiment results demonstrate that using the MC algorithms and the acceleration algorithms base on the VTK can implement 3-D visualization efficiently and satisfy practical applications.

CPU-GPU Rendering of CT Scan Images for Vertebra Reconstruction from CT Scan Images with a Calibration Policy

2020 ◽  
pp. 1-5
Author(s):  
Usman Khan ◽  
Usman Khan ◽  
AmanUllah Yasin ◽  
Imran Shafi ◽  
Muhammad Abid
Keyword(s):  
3D Reconstruction ◽  
Ct Scan ◽  
Surface Reconstruction ◽  
3D Visualization ◽  
Cervical Vertebra ◽  
Marching Cubes ◽  
Affine Transform ◽  
Visualization Techniques ◽  
Visualization Algorithms ◽  
Gpu Implementation

In this work GPU implementation of classic 3D visualization algorithms namely Marching Cubes and Raycasting has been carried for cervical vertebra using VTK libraries. A proposed framework has been introduced for efficient and duly calibrated 3D reconstruction using Dicom Affine transform and Python Mayavi framework to address the limitation of benchmark visualization techniques i.e. lack of calibration, surface reconstruction artifacts and latency.

scholarly journals Why a Virtual Assistant for Moral Enhancement When We Could have a Socrates?

2021 ◽  
Vol 27 (4) ◽  
Author(s):  
Francisco Lara
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
New Technologies ◽  
Personal Autonomy ◽  
Moral Enhancement ◽  
Moral Decisions ◽  
Educational Methodology ◽  
Better Than

AbstractCan Artificial Intelligence (AI) be more effective than human instruction for the moral enhancement of people? The author argues that it only would be if the use of this technology were aimed at increasing the individual's capacity to reflectively decide for themselves, rather than at directly influencing behaviour. To support this, it is shown how a disregard for personal autonomy, in particular, invalidates the main proposals for applying new technologies, both biomedical and AI-based, to moral enhancement. As an alternative to these proposals, this article proposes a virtual assistant that, through dialogue, neutrality and virtual reality technologies, can teach users to make better moral decisions on their own. The author concludes that, as long as certain precautions are taken in its design, such an assistant could do this better than a human instructor adopting the same educational methodology.

scholarly journals Gamification and Hazard Communication in Virtual Reality: A Qualitative Study

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4663
Author(s):  
Janaina Cavalcanti ◽  
Victor Valls ◽  
Manuel Contero ◽  
David Fonseca
Keyword(s):  
Virtual Reality ◽  
New Technologies ◽  
Training Environment ◽  
Hazard Communication ◽  
Head Mounted Display ◽  
Compliance Behavior ◽  
Before And After ◽  
Recorded Data ◽  
The Impact ◽  
Different Levels

An effective warning attracts attention, elicits knowledge, and enables compliance behavior. Game mechanics, which are directly linked to human desires, stand out as training, evaluation, and improvement tools. Immersive virtual reality (VR) facilitates training without risk to participants, evaluates the impact of an incorrect action/decision, and creates a smart training environment. The present study analyzes the user experience in a gamified virtual environment of risks using the HTC Vive head-mounted display. The game was developed in the Unreal game engine and consisted of a walk-through maze composed of evident dangers and different signaling variables while user action data were recorded. To demonstrate which aspects provide better interaction, experience, perception and memory, three different warning configurations (dynamic, static and smart) and two different levels of danger (low and high) were presented. To properly assess the impact of the experience, we conducted a survey about personality and knowledge before and after using the game. We proceeded with the qualitative approach by using questions in a bipolar laddering assessment that was compared with the recorded data during the game. The findings indicate that when users are engaged in VR, they tend to test the consequences of their actions rather than maintaining safety. The results also reveal that textual signal variables are not accessed when users are faced with the stress factor of time. Progress is needed in implementing new technologies for warnings and advance notifications to improve the evaluation of human behavior in virtual environments of high-risk surroundings.

scholarly journals SURG-03. IMMERSIVE VIRTUAL REALITY APPLICATIONS IN NEUROSURGICAL ONCOLOGY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii461-iii461
Author(s):  
Andrea Carai ◽  
Angela Mastronuzzi ◽  
Giovanna Stefania Colafati ◽  
Paul Voicu ◽  
Nicola Onorini ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
New Technologies ◽  
Young Patients ◽  
Spatial Relationship ◽  
3D Models ◽  
Immersive Virtual Reality ◽  
Global Improvement ◽  
3D Rendering ◽  
Before And After

Abstract Tridimensional (3D) rendering of volumetric neuroimaging is increasingly been used to assist surgical management of brain tumors. New technologies allowing immersive virtual reality (VR) visualization of obtained models offer the opportunity to appreciate neuroanatomical details and spatial relationship between the tumor and normal neuroanatomical structures to a level never seen before. We present our preliminary experience with the Surgical Theatre, a commercially available 3D VR system, in 60 consecutive neurosurgical oncology cases. 3D models were developed from volumetric CT scans and MR standard and advanced sequences. The system allows the loading of 6 different layers at the same time, with the possibility to modulate opacity and threshold in real time. Use of the 3D VR was used during preoperative planning allowing a better definition of surgical strategy. A tailored craniotomy and brain dissection can be simulated in advanced and precisely performed in the OR, connecting the system to intraoperative neuronavigation. Smaller blood vessels are generally not included in the 3D rendering, however, real-time intraoperative threshold modulation of the 3D model assisted in their identification improving surgical confidence and safety during the procedure. VR was also used offline, both before and after surgery, in the setting of case discussion within the neurosurgical team and during MDT discussion. Finally, 3D VR was used during informed consent, improving communication with families and young patients. 3D VR allows to tailor surgical strategies to the single patient, contributing to procedural safety and efficacy and to the global improvement of neurosurgical oncology care.

Sign in / Sign up

Export Citation Format

Share Document