Applications of virtual and augmented reality in biomedical imaging

[EN]Virtual and Augmented Reality has experienced a steady growth in medicine in recent years. At the same time, the radiological images play a central role in the diagnosis and planification of surgical approaches. The aim of this study is to present the first attempt to enhanced radiological image visualization using virtual and augmented reality for better planification and monitorization of surgeries. This application allows to move beyond traditional two-dimensional images towards three-dimensional models that can be visualized and manipulated with both Augmented Reality and Virtual Reality. We propose possible approaches to automate the segmentation of radiological images, using computer vision techniques and Artificial Intelligence

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

Dynamic Intracellular Metabolic Cell Signaling Profiles During Ag-Dependent B-Cell Differentiation

© 2021 Díez, Pérez-Andrés, Bøgsted, Azkargorta, García-Valiente, Dégano, Blanco, Mateos-Gomez, Bárcena, Santa Cruz, Góngora, Elortza, Landeira-Viñuela, Juanes-Velasco, Segura, Manzano-Román, Almeida, Dybkaer, Orfao and Fuentes.Human B-cell differentiation has been extensively investigated on genomic and transcriptomic grounds; however, no studies have accomplished so far detailed analysis of antigen-dependent maturation-associated human B-cell populations from a proteomic perspective. Here, we investigate for the first time the quantitative proteomic profiles of B-cells undergoing antigen-dependent maturation using a label-free LC-MS/MS approach applied on 5 purified B-cell subpopulations (naive, centroblasts, centrocytes, memory and plasma B-cells) from human tonsils (data are available via ProteomeXchange with identifier PXD006191). Our results revealed that the actual differences among these B-cell subpopulations are a combination of expression of a few maturation stage-specific proteins within each B-cell subset and maturation-associated changes in relative protein expression levels, which are related with metabolic regulation. The considerable overlap of the proteome of the 5 studied B-cell subsets strengthens the key role of the regulation of the stoichiometry of molecules associated with metabolic regulation and programming, among other signaling cascades (such as antigen recognition and presentation and cell survival) crucial for the transition between each B-cell maturation stage.We gratefully acknowledge financial support from the Spanish Health Institute Carlos III (ISCIII) for the grants: FIS PI14/01538, FIS PI17/01930 and CB16/12/00400. We also acknowledge Fondos FEDER (EU) and Junta Castilla-León (COVID19 grant COV20EDU/00187). Fundación Solórzano FS/38-2017.The Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0023, of the PE I + D + I 2017-2020, funded by ISCIII and FEDER. AL-V is supported by VIII Centenario-USAL PhD Program. PJ-V is supported by JCYL PhD Program and scholarship JCYL-EDU/601/2020. PD and EB are supported by a JCYL-EDU/346/2013 Ph.D. scholarship

MOOC “FORMACIÓN PRÁCTICA EN LA FORMACION INICIAL DE PROFESORADO DE SECUNDARIA”. Mejora de materiales docentes y Análisis del impacto de los cambios en la enseñanza al introducir el MOOC en el MUPES

Memoria ID-152. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2019-2020

Puesta en marcha de un curso MOOC denominado: formación práctica en la formación inicial de profesorado de secundaria (máster universitario en profesor de educación secundaria obligatoria, bachillerato, formación profesional y enseñanzas de idiomas

Memoria ID-146. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2018-2019

Espacios virtuales practicum MUPES. Utilización de todas las potencialidades de la nueva versión de Studium para mejorar la colaboración entre tutores de educación secundaria y tutores de la Universidad de Salamanca

Memoria ID-104. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2020-2021

El reto de la inclusión de los Objetivos de Desarrollo Sostenible en la formación inicial de profesores de secundaria: creación del MOOC curso cero sobre educación y ODS, inclusión en asignaturas y en trabajos fin de máster

Memoria ID-041. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2021-2022

Cualificación en los Objetivos establecidos en la Agenda 2030 de estudiantes y profesores en el Máster Universitario en Profesor de Educación Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanza de Idiomas (MUPES)

Memoria ID2022-157 Ayudas de la Universidad de Salamanca para la innovación docente, curso 2022-2023

Virtual Reality as an Educational and Training Tool for Medicine

Until very recently, we considered Virtual Reality as something that was very close, but it was still science fiction. However, today Virtual Reality is being integrated into many different areas of our lives, from videogames to different industrial use cases and, of course, it is starting to be used in medicine. There are two great general classifications for Virtual Reality. Firstly, we find a Virtual Reality in which we visualize a world completely created by computer, three-dimensional and where we can appreciate that the world we are visualizing is not real, at least for the moment as rendered images are improving very fast. Secondly, there is a Virtual Reality that basically consists of a reflection of our reality. This type of Virtual Reality is created using spherical or 360 images and videos, so we lose three-dimensional visualization capacity (until the 3D cameras are more developed), but on the other hand we gain in terms of realism in the images. We could also mention a third classification that merges the previous two, where virtual elements created by computer coexist with 360 images and videos. In this article we will show two systems that we have developed where each of them can be framed within one of the previous classifications, identifying the technologies used for their implementation as well as the advantages of each one. We will also analize how these systems can improve the current methodologies used for medical training. The implications of these developments as tools for teaching, learning and training are discussed.Sin financiación2.415 JCR (2018) Q2, 12/26 Medical Informatics, 41/98 Health Care Sciences & Services0.565 SJR (2018) Q2, 104/941 Information Systems, 11/57 Health Information Management, 28/163 Health InformaticsNo data IDR 2018UE