Medical students' cognitive load in volumetric image interpretation:Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum

Progression of White Matter Lesion Volume and Health-Related Quality of Life in Patients with Symptomatic Atherosclerotic Disease: The SMART-MR Study

Objectives. Mechanisms influencing the course of physical and mental functioning after an atherosclerotic event are unclear. We examined effects of white matter lesion (WML) activity on changes in functioning in patients with symptomatic atherosclerotic disease. Methods. In 486 patients (58 ± 9 years) of the Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study, volumetric WML measurements on 1.5T MRI were performed at baseline and 3.9 ± 0.4 years followup. Functioning was assessed with the modified Short-Form 12 (SF-12) questionnaire. Associations of WML progression with changes in functioning were adjusted for age, sex, and vascular risk factors. Results. Physical functioning (baseline: 44, 10th–90th percentile 29–55) improved, whereas mental functioning (baseline: 51, 10th–90th percentile 32–60) declined during followup. WML progression (highest quartile versus rest) contributed to a stronger decline in mental functioning (B = −1.76, 95% CI −3.11 to −0.42), but did not influence changes in physical functioning. Conclusions. Progression of WML volume contributes to a decline in mental functioning in patients with symptomatic atherosclerotic disease

Юрій Іллєнко (1936–2010)

15 червня помер Юрій Іллєнко – кінематографіст Божою милістю, чия творчість давно стала класикою вітчизняного і світового кіно

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

The virtual dissecting room : creating highly detailed anatomy models for educational purposes

INTRODUCTION: Virtual 3D models are powerful tools for teaching anatomy. At the present day, there are a lot of different digital anatomy models, most of these commercial applications are based on a 3D model of a human body reconstructed from images with a 1 millimeter intervals. The use of even smaller intervals may result in more details and more realistic appearances of 3D anatomy models. The aim of this study was to create a realistic and highly detailed 3D model of the hand and wrist based on small interval cross-sectional images, suitable for undergraduate and postgraduate teaching purposes with the possibility to perform a virtual dissection in an educational application. METHODS: In 115 transverse cross-sections from a human hand and wrist, segmentation was done by manually delineating 90 different structures. With the use of Amira the segments were imported and a surface model/polygon model was created, followed by smoothening of the surfaces in Mudbox. In 3D Coat software the smoothed polygon models were automatically retopologied into a quadrilaterals formation and a UV map was added. In Mudbox, the textures from 90 structures were depicted in a realistic way by using photos from real tissue and afterwards height maps, gloss and specular maps were created to add more level of detail and realistic lightning on every structure. Unity was used to build a new software program that would support all the extra map features together with a preferred user interface. CONCLUSION: A 3D hand model has been created, containing 100 structures (90 at start and 10 extra structures added along the way). The model can be used interactively by changing the transparency, manipulating single or grouped structures and thereby simulating a virtual dissection. This model can be used for a variety of teaching purposes, ranging from undergraduate medical students to residents of hand surgery. Studying the hand and wrist anatomy using this model is cost-effective and not hampered by the limited access to real dissecting facilities

The virtual dissecting room : creating highly detailed anatomy models for educational purposes

INTRODUCTION: Virtual 3D models are powerful tools for teaching anatomy. At the present day, there are a lot of different digital anatomy models, most of these commercial applications are based on a 3D model of a human body reconstructed from images with a 1 millimeter intervals. The use of even smaller intervals may result in more details and more realistic appearances of 3D anatomy models. The aim of this study was to create a realistic and highly detailed 3D model of the hand and wrist based on small interval cross-sectional images, suitable for undergraduate and postgraduate teaching purposes with the possibility to perform a virtual dissection in an educational application. METHODS: In 115 transverse cross-sections from a human hand and wrist, segmentation was done by manually delineating 90 different structures. With the use of Amira the segments were imported and a surface model/polygon model was created, followed by smoothening of the surfaces in Mudbox. In 3D Coat software the smoothed polygon models were automatically retopologied into a quadrilaterals formation and a UV map was added. In Mudbox, the textures from 90 structures were depicted in a realistic way by using photos from real tissue and afterwards height maps, gloss and specular maps were created to add more level of detail and realistic lightning on every structure. Unity was used to build a new software program that would support all the extra map features together with a preferred user interface. CONCLUSION: A 3D hand model has been created, containing 100 structures (90 at start and 10 extra structures added along the way). The model can be used interactively by changing the transparency, manipulating single or grouped structures and thereby simulating a virtual dissection. This model can be used for a variety of teaching purposes, ranging from undergraduate medical students to residents of hand surgery. Studying the hand and wrist anatomy using this model is cost-effective and not hampered by the limited access to real dissecting facilities