BookR: School of Health Core Facilities Booking
PubMed Publications| Keyword search (4,164 papers available) |  |
"Radiology" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Strengthening and Targeted Rehabilitation for Optimal Neuromuscular Gains for chronic BACK pain (STRONG-BACK): protocol for a randomised controlled trial in participants with primary nociceptive pain drivers | Fortin M; Rosenstein B; Bertrand C; Vaillancourt N; Wright A; Montpetit C; Macedo L; Elliott J; Cook CE; Tousignant-Laflamme Y; Ma J; Pagé MG; Dover G; Dang-Vu TT; Weber MH; | 41876162 SOH |
| 2 | Exploring interaction paradigms for segmenting medical images in virtual reality | Jones Z; Drouin S; Kersten-Oertel M; | 40402355 ENCS |
| Title: | Exploring interaction paradigms for segmenting medical images in virtual reality | ||||
| Authors: | Jones Z, Drouin S, Kersten-Oertel M | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/40402355/ | ||||
| DOI: | 10.1007/s11548-025-03424-y | ||||
| Publication: | International journal of computer assisted radiology and surgery | ||||
| Keywords: | Contours; Interaction methods; Radiology; Segmentation; Virtual reality; | ||||
| PMID: | 40402355 | Category: | Date Added: | 2025-05-22 | |
| Dept Affiliation: |
ENCS
1 Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, H3G 1M8, Canada. zacharyjonesmail@gmail.com. 2 Département de Génie Logiciel Et TI, École de Technologie Supérieure, 1100 R. Notre Dame O, Montreal, QC, H3C 1K3, Canada. 3 Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, H3G 1M8, Canada. |
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Description: |
Purpose: Virtual reality (VR) can offer immersive platforms for segmenting complex medical images to facilitate a better understanding of anatomical structures for training, diagnosis, surgical planning, and treatment evaluation. These applications rely on user interaction within the VR environment to manipulate and interpret medical data. However, the optimal interaction schemes and input devices for segmentation tasks in VR remain unclear. This study compares user performance and experience using two different input schemes. Methods: Twelve participants segmented 6 CT/MRI images using two input methods: keyboard and mouse (KBM) and motion controllers (MCs). Performance was assessed using accuracy, completion time, and efficiency. A post-task questionnaire measured users' perceived performance and experience. Results: No significant overall time difference was observed between the two input methods, though KBM was faster for larger segmentation tasks. Accuracy was consistent across input schemes. Participants rated both methods as equally challenging, with similar efficiency levels, but found MCs more enjoyable to use. Conclusion: These findings suggest that VR segmentation software should support flexible input options tailored to task complexity. Future work should explore enhancements to motion controller interfaces to improve usability and user experience. |
