Biomedical Visualisation

This edited book explores the use of technology to enable us to visualise the life sciences in a more meaningful and engaging way. It will enable those interested in visualisation techniques to gain a better understanding of the applications that can be used in visualisation, imaging and analysis, education, engagement and training. The reader will be able to explore the utilisation of technologies from a number of fields to enable an engaging and meaningful visual representation of the biomedical sciences, with a focus in this volume related to anatomy, and clinically applied scenarios. The first six chapters have an anatomical focus examining digital technologies and applications to enhance education. The first examines the history and development of ultrasound, applications in an educational setting, and as a point-of-care ultrasound at the bedside. The second chapter presents a transferable workflow methodology in creating an interactive educational and training package to enhance understanding of the circadian rhythm. The third chapter reviews tools and technologies, which can be used to enhance off-campus learning, and the current range of visualisation technologies like virtual, augmented and mixed reality systems. Chapter four discusses how scanning methodologies like CT imagery, can make stereoscopic models. The fifth chapter describes a novel way to reconstruct 3D anatomy from imaging datasets and how to build statistical 3D shape models, described in a clinical context and applied to diagnostic disease scoring. The sixth chapter looks at interactive visualisations of atlases in the creation of a virtual resource, for providing next generation interfaces. The seventh and eight chapters discuss neurofeedback for mental health education and interactive visual data analysis (applied to irritable bowel disease) respectively. The final two chapters examine current immersive technologies –virtual and augmented reality, with the last chapter detailing virtual reality in patients with dementia. This book is accessible to a wide range of users from faculty and students, developers and computing experts, the wider public audience. It is hoped this will aid understanding of the variety of technologies which can be used to enhance understanding of clinical conditions using modern day methodologies.

[1]  Jayaram K. Udupa,et al.  Imaging transforms for visualizing surfaces and volumes , 1993, Journal of Digital Imaging.

[2]  Dongmei Cui,et al.  Hips Don’t Lie: Expert Opinions Guide the Validation of a Virtual 3D Pelvis Model for Use in Anatomy Education and Medical Training , 2018, HAPS Educator.

[3]  Sara Ilstedt Hjelm,et al.  Brainball - using brain activity for cool competition , 2000 .

[4]  Stefan Zachow,et al.  Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative , 2019, Medical Image Anal..

[5]  Nicholas Ayache,et al.  Geometric Variability of the Scoliotic Spine Using Statistics on Articulated Shape Models , 2008, IEEE Transactions on Medical Imaging.

[6]  Ghassan Hamarneh,et al.  A Survey on Shape Correspondence , 2011, Comput. Graph. Forum.

[7]  F. Govsa,et al.  Creating vascular models by postprocessing computed tomography angiography images: a guide for anatomical education , 2017, Surgical and Radiologic Anatomy.

[8]  Richard Baldock,et al.  eMouseAtlas, EMAGE, and the spatial dimension of the transcriptome , 2012, Mammalian Genome.

[9]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[10]  Stefan Zachow,et al.  Fully Automated and Highly Accurate Dense Correspondence for Facial Surfaces , 2016, ECCV Workshops.

[11]  P. McKeown,et al.  The impact of curricular change on medical students' knowledge of anatomy , 2003, Medical education.

[12]  Hans Lamecker,et al.  3D Shape Analysis for Coarctation of the Aorta , 2018, ShapeMI@MICCAI.

[13]  E. Friedrich,et al.  An Effective Neurofeedback Intervention to Improve Social Interactions in Children with Autism Spectrum Disorder , 2015, Journal of Autism and Developmental Disorders.

[14]  Tarek M. Abdelhamid.  The Multidimensional Learning Model: A Novel Cognitive Psychology-Based Model for Computer Assisted Instruction in Order to Improve Learning in Medical Students , 1999 .

[15]  Thilo Hinterberger,et al.  The Sensorium: A Multimodal Neurofeedback Environment , 2011, Adv. Hum. Comput. Interact..

[16]  Joe Michael Kniss,et al.  A Model for Volume Lighting and Modeling , 2003, IEEE Trans. Vis. Comput. Graph..

[17]  Michael P. Chae,et al.  3D‐Printed haptic “Reverse” models for preoperative planning in soft tissue reconstruction: A case report , 2015, Microsurgery.

[18]  Anders Ynnerman,et al.  Local Ambient Occlusion in Direct Volume Rendering , 2010, IEEE Transactions on Visualization and Computer Graphics.

[19]  Ingrid Hotz,et al.  Feature Level-Sets: Generalizing Iso-Surfaces to Multi-Variate Data , 2020, IEEE Transactions on Visualization and Computer Graphics.

[20]  M. Clarkson.  Representation of anatomy in online atlases and databases: a survey and collection of patterns for interface design , 2016, BMC Developmental Biology.

[21]  Z. Segal,et al.  Mindfulness: A Proposed Operational Definition , 2004 .

[22]  W. Pawlina,et al.  An update on the status of anatomical sciences education in United States medical schools , 2014, Anatomical sciences education.

[23]  Timothy D Wilson,et al.  A head in virtual reality: Development of a dynamic head and neck model , 2009, Anatomical sciences education.

[24]  Chiara Eva Catalano,et al.  Best Practices for an Effective Design and Evaluation of Serious Games , 2014 .

[25]  T. D. Wilson,et al.  Evaluation of the effectiveness of 3D vascular stereoscopic models in anatomy instruction for first year medical students , 2017, Anatomical sciences education.

[26]  S Zachow,et al.  Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative. , 2018, Osteoarthritis and cartilage.

[27]  Hans-Christian Hege,et al.  Fast Generation of Virtual X-ray Images for Reconstruction of 3D Anatomy , 2013, IEEE Transactions on Visualization and Computer Graphics.

[28]  L. Huang-Storms.  Efficacy of neurofeedback for children with histories of abuse and neglect: Pilot study and meta-analytic comparison to other treatments. , 2008 .

[29]  D. Yeh,et al.  Improving Learning Efficiency of Factual Knowledge in Medical Education. , 2015, Journal of surgical education.

[30]  R Gilberto González,et al.  Magnetic resonance angiography: physical principles and applications. , 2016, Handbook of clinical neurology.

[31]  Kevin Fung,et al.  Three-dimensional educational computer model of the larynx: voicing a new direction. , 2009, Archives of otolaryngology--head & neck surgery.

[32]  Piet Kommers,et al.  Optimizing conditions for computer-assisted anatomical learning , 2006, Interact. Comput..

[33]  S. Zachow,et al.  Changes in knee shape and geometry resulting from total knee arthroplasty , 2018, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[34]  Nils-Claudius Gellrich,et al.  Modellgestützte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen , 2010 .

[35]  Ivan Viola,et al.  DimSUM: Dimension and Scale Unifying Map for Visual Abstraction of DNA Origami Structures , 2018, Comput. Graph. Forum.

[36]  Henrik Ohlsson,et al.  A Brain Computer Interface for Communication Using Real-Time fMRI , 2010, 2010 20th International Conference on Pattern Recognition.

[37]  M. Proctor,et al.  Three-Dimensional Display Technologies for Anatomical Education: A Literature Review , 2016 .

[38]  T. Egner,et al.  Foundation and Practice of Neurofeedback for the Treatment of Epilepsy , 2006, Applied psychophysiology and biofeedback.

[39]  S. DiCarlo,et al.  Too much teaching, not enough learning: what is the solution? , 2006, Advances in physiology education.

[40]  Li Sheng-mei,et al.  On the Structure of "秀才秀才,错字布袋 , 2003 .

[41]  Allan R. Jones,et al.  A mesoscale connectome of the mouse brain , 2014, Nature.

[42]  Christopher Dooley,et al.  The Impact of Meditative Practices on Physiology and Neurology: A Review of the Literature , 2009 .

[43]  Henrik Ohlsson,et al.  Concurrent Volume Visualization of Real-Time fMRI , 2010, VG@Eurographics.

[44]  David S. Ebert,et al.  Illustration motifs for effective medical volume illustration , 2005, IEEE Computer Graphics and Applications.

[45]  B. Rothbaum,et al.  The Use of Virtual Reality Technology in the Treatment of Anxiety and Other Psychiatric Disorders , 2017, Harvard review of psychiatry.

[46]  Valentina Agostini,et al.  Segmentation and Classification of Gait Cycles , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[47]  Simon Hein,et al.  Stereoscopic (3D) versus monoscopic (2D) laparoscopy: comparative study of performance using advanced HD optical systems in a surgical simulator model , 2016, World Journal of Urology.

[48]  Martin Trapp,et al.  3D object retrieval in an atlas of neuronal structures , 2013, The Visual Computer.

[49]  W. Pawlina,et al.  Medical education in the anatomical sciences: The winds of change continue to blow , 2009, Anatomical sciences education.

[50]  J. Assmus,et al.  Neurofeedback for the treatment of children and adolescents with ADHD: a randomized and controlled clinical trial using parental reports , 2012, BMC Psychiatry.

[51]  Ivan Viola,et al.  Pondering the Concept of Abstraction in (Illustrative) Visualization , 2018, IEEE Transactions on Visualization and Computer Graphics.

[52]  David Salesin,et al.  Automated generation of interactive 3D exploded view diagrams , 2008, ACM Trans. Graph..

[53]  Hans-Christian Hege,et al.  An articulated statistical shape model for accurate hip joint segmentation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[54]  Melvyn A. Goodale,et al.  The role of binocular vision in prehension: a kinematic analysis , 1992, Vision Research.

[55]  Timo Ropinski,et al.  Inviwo — A Visualization System with Usage Abstraction Levels , 2018, IEEE Transactions on Visualization and Computer Graphics.

[56]  Timothy D Wilson,et al.  Comparison of 3D reconstructive technologies used for morphometric research and the translation of knowledge using a decision matrix , 2013, Anatomical sciences education.

[57]  Timo Ropinski,et al.  Multimodal volume illumination , 2015, Comput. Graph..

[58]  Timothy F. Cootes,et al.  A Minimum Description Length Approach to Statistical Shape Modelling , 2001 .

[59]  Luiz Velho,et al.  Warping and morphing of graphical objects , 1998 .

[60]  C D Wickens,et al.  Implications of Graphics Enhancements for the Visualization of Scientific Data: Dimensional Integrality, Stereopsis, Motion, and Mesh , 1994, Human factors.

[61]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[62]  G. Woodman.  A brief introduction to the use of event-related potentials in studies of perception and attention. , 2010, Attention, perception & psychophysics.

[63]  James Paul Gee,et al.  Game–like learning: An example of situated learning and implications for opportunity to learn , 2008 .

[64]  J. McQuaid,et al.  The Effects of Mindfulness Meditation on Cognitive Processes and Affect in Patients with Past Depression , 2004, Cognitive Therapy and Research.

[65]  Rohit Chopra,et al.  MeditAid: a wearable adaptive neurofeedback-based system for training mindfulness state , 2015, Personal and Ubiquitous Computing.

[66]  Chris Weaver.  Building Highly-Coordinated Visualizations in Improvise , 2004, IEEE Symposium on Information Visualization.

[67]  Aaron Oliker,et al.  The BioDigital Human: A Web-based 3D Platform for Medical Visualization and Education , 2012, MMVR.

[68]  Stefan Bruckner,et al.  Visualization and Quantification for Interactive Analysis of Neural Connectivity in Drosophila , 2017, Comput. Graph. Forum.

[69]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[70]  Adam Finkelstein,et al.  Line drawings from volume data , 2005, ACM Trans. Graph..

[71]  Maria A. Zuluaga,et al.  Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches , 2017, IEEE Transactions on Biomedical Engineering.

[72]  Daniel B. Carr,et al.  Scatterplot matrix techniques for large N , 1986 .

[73]  Vincent Hayward,et al.  Wearable Haptic Systems for the Fingertip and the Hand: Taxonomy, Review, and Perspectives , 2017, IEEE Transactions on Haptics.

[74]  Aitor Moreno,et al.  Interactive visualization of volumetric data with WebGL in real-time , 2011, Web3D '11.

[75]  Allan R. Jones,et al.  The Allen Brain Atlas: 5 years and beyond , 2009, Nature Reviews Neuroscience.

[76]  Timothy D Wilson,et al.  Explorable three‐dimensional digital model of the female pelvis, pelvic contents, and perineum for anatomical education , 2010, Anatomical sciences education.

[77]  Hans-Christian Hege,et al.  A 3D statistical shape model of the pelvic bone for segmentation , 2004, SPIE Medical Imaging.

[78]  Marc Levoy,et al.  Display of surfaces from volume data , 1988, IEEE Computer Graphics and Applications.

[79]  Ivan Viola,et al.  Focus of attention+context and smart visibility in visualization , 2005, SIGGRAPH Courses.

[80]  Cristian Lorenz,et al.  3D reconstruction of the human rib cage from 2D projection images using a statistical shape model , 2010, International Journal of Computer Assisted Radiology and Surgery.

[81]  Stefan Zachow,et al.  Reconstruction of mandibular dysplasia using a statistical 3D shape model , 2005 .

[82]  Matthew D B S Tam,et al.  Building virtual models by postprocessing radiology images: A guide for anatomy faculty , 2010, Anatomical sciences education.

[83]  M. Morgan,et al.  Grasping deficits and adaptations in adults with stereo vision losses. , 2009, Investigative ophthalmology & visual science.

[84]  I. Whitaker,et al.  The financial implications of computed tomographic angiography in DIEP flap surgery: A cost analysis , 2009, Microsurgery.

[85]  Anne E Carpenter,et al.  Visualization of image data from cells to organisms , 2010, Nature Methods.

[86]  Heidi Phillips,et al.  3D micro-CT imaging of the postmortem brain , 2008, Journal of Neuroscience Methods.

[87]  Stefan Zachow,et al.  Frame-based cranial reconstruction. , 2014, Journal of neurosurgery. Pediatrics.

[88]  Mark Terrell,et al.  Anatomy of learning: instructional design principles for the anatomical sciences. , 2006, Anatomical record. Part B, New anatomist.

[89]  Christina Amaxopoulou,et al.  Stereoscopic neuroanatomy lectures using a three-dimensional virtual reality environment. , 2015, Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft.

[90]  G. Westheimer.  SEEING DEPTH WITH TWO EYES: STEREOPSIS , 1994 .

[91]  Thomas Ertl,et al.  Interactive Cutaway Illustrations , 2003, Comput. Graph. Forum.

[92]  B. A. Conway,et al.  The effects of laforin, malin, Stbd1, and Ptg deficiencies on heart glycogen levels in Pompe disease mouse models , 2015 .

[93]  James Paul Gee,et al.  Learning by Design: Good Video Games as Learning Machines , 2005 .

[94]  Iis P. Tussyadiah,et al.  Virtual reality, presence, and attitude change: Empirical evidence from tourism , 2018, Tourism Management.

[95]  Timo Ropinski,et al.  A Survey of Volumetric Illumination Techniques for Interactive Volume Rendering , 2014, Comput. Graph. Forum.

[96]  Michael Stiller,et al.  Medical applications for statistical shape models , 2005 .

[97]  J. Sañudo,et al.  Meaning and clinical interest of the anatomical variations in the 21 st century , 2022 .

[98]  Jonathan C. Roberts,et al.  Visual comparison for information visualization , 2011, Inf. Vis..

[99]  T. D. Wilson,et al.  Stereoscopic vascular models of the head and neck: A computed tomography angiography visualization , 2016, Anatomical sciences education.

[100]  P. Milgram,et al.  A Taxonomy of Mixed Reality Visual Displays , 1994 .

[101]  Benoit M. Dawant,et al.  An atlas-based method to compensate for brain shift: Preliminary results , 2007, Medical Image Anal..

[102]  Kup-Sze Choi,et al.  Alternatives to relational database: Comparison of NoSQL and XML approaches for clinical data storage , 2013, Comput. Methods Programs Biomed..

[103]  K. Edwards,et al.  What anatomy is clinically useful and when should we be teaching it? , 2016, Anatomical sciences education.

[104]  Jens Schneider,et al.  ClearView: An Interactive Context Preserving Hotspot Visualization Technique , 2006, IEEE Transactions on Visualization and Computer Graphics.

[105]  Meritxell Bach Cuadra,et al.  A review of atlas-based segmentation for magnetic resonance brain images , 2011, Comput. Methods Programs Biomed..

[106]  Stefan Zachow,et al.  Accurate Automated Volumetry of Cartilage of the Knee Using Convolutional Neural Networks: Data From the Osteoarthritis Initiative , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[107]  Ivan Viola,et al.  Importance-driven feature enhancement in volume visualization , 2005, IEEE Transactions on Visualization and Computer Graphics.

[108]  Christoph von Tycowicz,et al.  An efficient Riemannian statistical shape model using differential coordinates: With application to the classification of data from the Osteoarthritis Initiative , 2018, Medical Image Anal..

[109]  Giuseppe Fico,et al.  A Succinct Overview of Virtual Reality Technology Use in Alzheimer’s Disease , 2015, Front. Aging Neurosci..

[110]  Jennifer C Molloy,et al.  The Open Knowledge Foundation: Open Data Means Better Science , 2011, PLoS biology.

[111]  Thomas Lange,et al.  A Statistical Shape Model for the Liver , 2002, MICCAI.

[112]  Y. Fujimoto,et al.  Evaluation of two- and three-dimensional visualization for endoscopic endonasal surgery using a novel stereoendoscopic system in a novice: a comparison on a dry laboratory model , 2013, Acta Neurochirurgica.

[113]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[114]  Elmar Eisemann,et al.  The Online Anatomical Human: Web-based Anatomy Education , 2016, Eurographics.

[115]  David S. Ebert,et al.  Non-photorealistic volume rendering using stippling techniques , 2002, IEEE Visualization, 2002. VIS 2002..

[116]  S. Puli,et al.  Irritable Bowel Syndrome: A Clinical Review. , 2016, Current rheumatology reviews.

[117]  Matthieu Poyade,et al.  Proof of concept of a workflow methodology for the creation of basic canine head anatomy veterinary education tool using augmented reality , 2018, PloS one.

[118]  Alessandro Rizzi,et al.  Assessing stereo blindness and stereo acuity on digital displays , 2014, Displays.

[119]  Stefan Bruckner,et al.  Interactive Dynamic Volume Illumination with Refraction and Caustics , 2018, IEEE Transactions on Visualization and Computer Graphics.

[120]  T. Poggio,et al.  The analysis of stereopsis. , 1984, Annual review of neuroscience.

[121]  Min Chen,et al.  Feature Aligned Volume Manipulation for Illustration and Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

[122]  Marcel Breeuwer,et al.  CoViCAD: Comprehensive Visualization of Coronary Artery Disease , 2007, IEEE Transactions on Visualization and Computer Graphics.

[123]  P. Watson,et al.  Mystical Experience Among Tibetan Buddhists: The Common Core Thesis Revisited , 2011 .

[124]  C. Daly King,et al.  The Meaning of Normal * , 1945, The Yale journal of biology and medicine.

[125]  Russell H. Taylor,et al.  Statistical Atlases of Bone Anatomy: Construction, Iterative Improvement and Validation , 2007, MICCAI.

[126]  Barbara Wasson,et al.  Mnemosyne: Adapting the Method of Loci to Immersive Virtual Reality , 2018, AVR.

[127]  P. Nair,et al.  A large scale finite element study of a cementless osseointegrated tibial tray. , 2013, Journal of biomechanics.

[128]  B. Tomandl,et al.  CT angiography of intracranial aneurysms: a focus on postprocessing. , 2004, Radiographics : a review publication of the Radiological Society of North America, Inc.

[129]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[130]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[131]  John P. McIntire,et al.  Stereoscopic 3D displays and human performance: A comprehensive review , 2014, Displays.

[132]  Russell H. Taylor,et al.  A statistical bone density atlas and deformable medical image registration , 2002 .

[133]  Catherine Plaisant,et al.  Visualizing Missing Data: Graph Interpretation User Study , 2005, INTERACT.

[134]  P. Slade,et al.  Depth of information processing and memory for medical facts , 1995 .

[135]  Joseph Ross Mitchell,et al.  Real-Time Super Resolution Contextual Close-up of Clinical Volumetric Data , 2006, EuroVis.

[136]  B. Rothbaum,et al.  Virtual Reality-Enhanced Extinction of Phobias and Post-Traumatic Stress , 2017, Neurotherapeutics.

[137]  Pat Hanrahan,et al.  Visualization of Heterogeneous Data , 2007, IEEE Transactions on Visualization and Computer Graphics.

[138]  Daniel F. Keefe,et al.  Comparison techniques utilized in spatial 3D and 4D data visualizations: A survey and future directions , 2017, Comput. Graph..

[139]  Stefan Bruckner,et al.  Semantic Layers for Illustrative Volume Rendering , 2007, IEEE Transactions on Visualization and Computer Graphics.

[140]  Hans Lamecker,et al.  Variational and statistical shape modeling for 3D geometry reconstruction , 2008 .

[141]  Stefan Bruckner,et al.  TECHNICAL REPORT VolumeShop: An Interactive System for Direct Volume , 2022 .

[142]  Stefan Bruckner,et al.  Illustrative Context-Preserving Exploration of Volume Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[143]  Xin Ma,et al.  The clinical efficacy of reminiscence therapy in patients with mild-to-moderate Alzheimer disease , 2017, Medicine.

[144]  Hans-Christian Hege,et al.  3D Reconstruction of Individual Anatomy from Medical Image Data: Segmentation and Geometry Processing , 2007 .

[145]  William Perrotti,et al.  From college to clinic: Reasoning over memorization is key for understanding anatomy , 2002, The Anatomical record.

[146]  Paul Rosen,et al.  From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches , 2011, WoCoUQ.

[147]  Stefan Zachow,et al.  Statistical Shape Modeling of Musculoskeletal Structures and Its Applications , 2016 .

[148]  Stefan Bruckner,et al.  BrainGazer - Visual Queries for Neurobiology Research , 2009, IEEE Transactions on Visualization and Computer Graphics.

[149]  Charl P. Botha,et al.  A Unified Representation for the Model-based Visualization of Heterogeneous Anatomy Data , 2012, EuroVis.

[150]  Kevin Fung,et al.  Development of a computer‐assisted cranial nerve simulation from the visible human dataset , 2011, Anatomical sciences education.

[151]  K. Jones,et al.  Smith's Recognizable Patterns of Human Malformation , 1996 .

[152]  M. Khan,et al.  Visualization of stereoscopic anatomic models of the paranasal sinuses and cervical vertebrae from the surgical and procedural perspective , 2017, Anatomical sciences education.

[153]  M. Lages,et al.  Screening and sampling in studies of binocular vision , 2012, Vision Research.

[154]  H. Lameckera,et al.  Surgical Treatment of Craniosynostosis based on a Statistical 3 D-Shape Model : First Clinical Application , 2006 .

[155]  Jay David Bolter,et al.  MRX: an interdisciplinary framework for mixed reality experience design and criticism , 2015, Digit. Creativity.

[156]  Timothy D Wilson,et al.  The development of a virtual 3D model of the renal corpuscle from serial histological sections for E‐learning environments , 2015, Anatomical sciences education.

[157]  Hans-Christian Hege,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[158]  Bertram J. Unger,et al.  Importance of Stereoscopy in Haptic Training of Novice Temporal Bone Surgery , 2016, MMVR.

[159]  Fumio Kishino,et al.  Augmented reality: a class of displays on the reality-virtuality continuum , 1995, Other Conferences.

[160]  Steven K. Feiner,et al.  Cutaways and ghosting: satisfying visibility constraints in dynamic 3D illustrations , 1992, The Visual Computer.

[161]  Maged N Kamel Boulos,et al.  Head-Mounted Virtual Reality and Mental Health: Critical Review of Current Research , 2018, JMIR serious games.

[162]  Anders Ynnerman,et al.  Correlated Photon Mapping for Interactive Global Illumination of Time-Varying Volumetric Data , 2017, IEEE Transactions on Visualization and Computer Graphics.

[163]  Thomas Lange,et al.  Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model , 2007 .

[164]  Cristian Lorenz,et al.  Temporal subtraction of chest radiographs compensating pose differences , 2011, Medical Imaging.

[165]  Allan R. Jones,et al.  An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.

[166]  Stefan Schmidt,et al.  Measuring mindfulness—the Freiburg Mindfulness Inventory (FMI) , 2006 .

[167]  F. Bookstein.  Size and Shape Spaces for Landmark Data in Two Dimensions , 1986 .

[168]  Piet Kommers,et al.  The role of stereopsis in virtual anatomical learning , 2008, Interact. Comput..

[169]  H. P. Friedman,et al.  The surgical implications of physiologic patterns in myocardial infarction shock. , 1972, Surgery.

[170]  Bernhard Ruthensteiner,et al.  Soft Part 3D visualization by serial sectioning and computer reconstruction , 2008 .

[171]  J. Kooloos,et al.  Stereopsis, Visuospatial Ability, and Virtual Reality in Anatomy Learning , 2017, Anatomy research international.

[172]  Mukund Raj,et al.  Evaluating Shape Alignment via Ensemble Visualization , 2016, IEEE Computer Graphics and Applications.

[173]  V. Spitzer,et al.  The visible human male: a technical report. , 1996, Journal of the American Medical Informatics Association : JAMIA.

[174]  Stefan Bruckner,et al.  Exploded Views for Volume Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[175]  Stefan Bruckner,et al.  Style Transfer Functions for Illustrative Volume Rendering , 2007, Comput. Graph. Forum.

[176]  Frank E. Pollick,et al.  Enheduanna – A Manifesto of Falling: first demonstration of a live brain-computer cinema performance with multi-brain BCI interaction for one performer and two audience members , 2017, Digit. Creativity.

[177]  Hristoph Von,et al.  A Shape Trajectories Approach to Longitudinal Statistical Analysis , 2018 .

[178]  Johan Thunberg,et al.  Shape‐aware surface reconstruction from sparse 3D point‐clouds , 2016, Medical Image Anal..

[179]  Hans-Christian Hege,et al.  TADD: A Computational Framework for Data Analysis Using Discrete Morse Theory , 2010, ICMS.

[180]  Tomás Ward,et al.  A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[181]  Guido Gerig,et al.  Longitudinal modeling of appearance and shape and its potential for clinical use , 2016, Medical Image Anal..

[182]  Paul Rea,et al.  A recommended workflow methodology in the creation of an educational and training application incorporating a digital reconstruction of the cerebral ventricular system and cerebrospinal fluid circulation to aid anatomical understanding , 2015 .

[183]  David Metcalf,et al.  A Digital Brain Atlas for Surgical Planning, Model-Driven Segmentation, and Teaching , 1996, IEEE Trans. Vis. Comput. Graph..

[184]  Ajay Das,et al.  Overview of Attention Deficit Hyperactivity Disorder in Young Children , 2015, Health psychology research.

[185]  J. Banquet.  Spectral analysis of the EEG in meditation. , 1973, Electroencephalography and clinical neurophysiology.

[186]  T. K. Carne,et al.  Shape and Shape Theory , 1999 .

[187]  Bernhard Preim,et al.  A survey of virtual human anatomy education systems , 2018, Comput. Graph..

[188]  Heidrun Schumann,et al.  Interactive Lenses for Visualization: An Extended Survey , 2017, Comput. Graph. Forum.

[189]  A. Jha,et al.  Mindfulness training modifies subsystems of attention , 2007, Cognitive, affective & behavioral neuroscience.

[190]  Allan R. Jones,et al.  The Allen Human Brain Atlas Comprehensive gene expression mapping of the human brain , 2012, Trends in Neurosciences.

[191]  Peter Willemsen,et al.  Does the Quality of the Computer Graphics Matter when Judging Distances in Visually Immersive Environments? , 2004, Presence: Teleoperators & Virtual Environments.

[192]  Hans-Christian Hege,et al.  An Articulated Statistical Shape Model of the Human Knee , 2011, Bildverarbeitung für die Medizin.

[193]  R. Leahy,et al.  Digimouse: a 3D whole body mouse atlas from CT and cryosection data , 2007, Physics in medicine and biology.

[194]  James J. Thomas,et al.  Defining Insight for Visual Analytics , 2009, IEEE Computer Graphics and Applications.

[195]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[196]  Amy Hilbelink,et al.  A measure of the effectiveness of incorporating 3D human anatomy into an online undergraduate laboratory , 2009, Br. J. Educ. Technol..

[197]  Kai Lawonn,et al.  A Survey of Surface‐Based Illustrative Rendering for Visualization , 2018, Comput. Graph. Forum.

[198]  E. Birch,et al.  The functional significance of stereopsis. , 2010, Investigative ophthalmology & visual science.

[199]  Kai Lawonn,et al.  PelVis: Atlas-based Surgical Planning for Oncological Pelvic Surgery , 2017, IEEE Transactions on Visualization and Computer Graphics.

[200]  Cristian Lorenz,et al.  Spine Segmentation Using Articulated Shape Models , 2008, MICCAI.