Evaluation of 3D slicer as a medical virtual reality visualization platform
Bibliographic record
Abstract
PURPOSE: There is a lack of open-source or free virtual reality (VR) software that can be utilized for research by medical professionals and researchers. We propose the design and implementation of such software. We also aim to assess the feasibility of using VR as a modality for navigating 3D visualizations of medical scenes. METHODS: To achieve our goal, we added VR capabilities to the open-source medical image analysis and visualization platform, 3D Slicer. We designed the VR extension by basing the software architecture on VTK’s vtkRenderingOpenVR software module. We extended this module by adding features such as full interactivity between 3D Slicer and the VR extension during VR use, variable volume rendering quality based on user headset motion etc. Furthermore, the VR extension was tested in a feasibility study in which participants were asked to complete specific tasks using bot the conventional mouse-monitor and VR method. For this experiment, we used 3D Slicer to create two virtual settings, each having an associated task. Participants were asked to maneuver the virtual settings using two approaches, the conventional method, using mouse and monitor, and VR using the head-mounted-display and controllers. The main outcome measure was total time to complete the task. RESULTS: We developed a VR extension to 3D Slicer—SlicerVirtualReality (SlicerVR). Additionally, from the experiment we conducted we found that when comparing mean completion times, participants, when using VR, were able to complete the first task 3 minutes and 28 seconds quicker than the mouse and monitor method (4 minutes and 24 seconds vs. 7 minutes and 52 seconds, respectively); and the second task 1 minute and 20 seconds quicker (2 minutes and 37 seconds, vs. 3 minutes and 57 seconds, respectively). CONCLUSION: We augmented the 3D Slicer platform with virtual reality capabilities. Experiments results show a considerable improvement in time required to navigate and complete tasks within complex virtual scenes compared to the traditional mouse and monitor method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".