On the Use of Virtual Reality for Medical Imaging Visualization

Pires, Filipe; Costa, Carlos; Dias, Paulo

doi:10.1007/s10278-021-00480-z

Cited by 20 publications

(15 citation statements)

References 43 publications

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“…Compared with other simulators, the simulator used in this study has the advantage of being developed by a doctor who performs and teaches the procedure [24,26]. Although there have been many advances in attempts to reproduce reality using VR, this is still a challenging task due to limitations in the performance and pricing of equipment.…”

Section: Discussionmentioning

confidence: 99%

Virtual reality simulator’s effectiveness on the spine procedure education for trainee: a randomized controlled trial

Kim¹,

Lee²,

Lee

et al. 2023

Korean J Anesthesiol

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Section: Discussionmentioning

confidence: 99%

Virtual reality simulator’s effectiveness on the spine procedure education for trainee: a randomized controlled trial

Kim¹,

Lee²,

Lee

et al. 2023

Korean J Anesthesiol

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“…One of the most challenging aspects of processing healthcare images is understanding the image data format. The main file formats now employed in medical imaging are Analyze, Neuroimaging Informatics Technology Initiative (Nifti), Minc, and Digital Imaging and Communications in Medicine (DICOM) [ 350 ].…”

Section: Workflowmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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“…A potential way to overcome the disadvantages of VSP performed in a DS environment could be the use of virtual reality (VR) [ 22 ] since it offers stereoscopic vision, allows the user to work manually, and allows more focused work due to immersion [ 21 , 23 ]. VR environments better resemble realistic work routines in the operating theater, as users can rotate and flip anatomical structures and observe different intraoperative viewing angles [ 24 ], thus enabling an enhanced understanding of the anatomy [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…VR simulations have been shown to lead to an improvement in surgical skills among subjects [ 29 ]. VR was also used for the visualization of medical images in radiology [ 22 ]. Recently, VR has been used for spatial understanding [ 30 ] or in the course of multiuser conferences during surgical planning [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study

Ulbrich¹,

Bosch²,

Bönsch³

et al. 2023

JMIR Serious Games

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Background As an integral part of computer-assisted surgery, virtual surgical planning (VSP) leads to significantly better surgery results, such as for oral and maxillofacial reconstruction with microvascular grafts of the fibula or iliac crest. It is performed on a 2D computer desktop screen (DS) based on preoperative medical imaging. However, in this environment, VSP is associated with shortcomings, such as a time-consuming planning process and the requirement of a learning process. Therefore, a virtual reality (VR)–based VSP application has great potential to reduce or even overcome these shortcomings due to the benefits of visuospatial vision, bimanual interaction, and full immersion. However, the efficacy of such a VR environment has not yet been investigated. Objective This study aimed to demonstrate the possible advantages of a VR environment through a substep of VSP, specifically the segmentation of the fibula (calf bone) and os coxae (hip bone), by conducting a training course in both DS and VR environments and comparing the results. Methods During the training course, 6 novices were taught how to use a software application in a DS environment (3D Slicer) and in a VR environment (Elucis) for the segmentation of the fibula and os coxae, and they were asked to carry out the maneuvers as accurately and quickly as possible. Overall, 13 fibula and 13 os coxae were segmented for each participant in both methods (VR and DS), resulting in 156 different models (78 fibula and 78 os coxae) per method (VR and DS) and 312 models in total. The individual learning processes in both environments were compared using objective criteria (time and segmentation performance) and self-reported questionnaires. The models resulting from the segmentation were compared mathematically (Hausdorff distance and Dice coefficient) and evaluated by 2 experienced radiologists in a blinded manner. Results A much faster learning curve was observed for the VR environment than the DS environment (β=.86 vs β=.25). This nearly doubled the segmentation speed (cm3/min) by the end of training, leading to a shorter time (P<.001) to reach a qualitative result. However, there was no qualitative difference between the models for VR and DS (P=.99). The VR environment was perceived by participants as more intuitive and less exhausting, and was favored over the DS environment. Conclusions The more rapid learning process and the ability to work faster in the VR environment could save time and reduce the VSP workload, providing certain advantages over the DS environment.

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On the Use of Virtual Reality for Medical Imaging Visualization

Cited by 20 publications

References 43 publications

Virtual reality simulator’s effectiveness on the spine procedure education for trainee: a randomized controlled trial

Virtual reality simulator’s effectiveness on the spine procedure education for trainee: a randomized controlled trial

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Advantages of a Training Course for Surgical Planning in Virtual Reality for Oral and Maxillofacial Surgery: Crossover Study

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