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DeformView: Quantitative Visualization of Non-Linear Deformation Fields for Use in Image-Guided Neurosurgery

Key Investigators

Project Description

We have been developing DeformView, a visualization module for 3D Slicer that improves the interpretation of non-linear brain deformation (“brain shift”) during image-guided neurosurgery and as a training tool for inexperienced surgeons and researchers. DeformView provides two dense, intuitive visualization maps: (1) a dense displacement magnitude map (mm), and (2) a Jacobian determinant magnitude map representing local tissue expansion and compression (%).

The proposed module combines scientifically derived, intuitive colour maps and voxel-wise cursor pointer that directly displays displacement values on hover, a function not available in existing Slicer tools, to improve user understanding and confidence.

Objective

  1. Objective A. Improve user experience and stability by identifying and fixing bugs, refining interactions, and ensuring reliable performance across datasets.
  2. Objective B. Gather user feedback from researchers and clinicians to guide the design of additional features, including potentially adding features to visualize registration error and uncertainty within the module).
  3. Objective C. Integrate transform grid/ glyph visualizations directly into DeformView to provide complementary spatial context alongside dense deformation maps.

Approach and Plan

  1. We will systematically test DeformView across representative datasets (focusing on IGNS-focused data - ReMIND, RESECT, BITE, etc.) and use cases to identify and resolve software bugs. We will ask attendees to use the module to identify common workflows, areas of improvement. We will also perform stress and destructive testing.
  2. User-centered design and feedback: We will conduct structured feedback sessions with expert users, non-expert users, and clinicians, using our targeted questionnaires and short tasks to identify desired features and usability gaps. We will lead discussions with attendees to identify areas of improvement and feature prioritization.

Progress and Next Steps

Progress

We have implemented the core functionality of the DeformView module, including dense deformation visualization, Jacobian-based expansion/compression maps, and voxel-wise readout on cursor hover. Initial testing confirms that primary visualization goals have been achieved, with only minor usability and stability issues remaining.

We conducted a user study with 10 non-expert participants (average 2.9 years of imaging research experience) to evaluate module functionality. Participants compared DeformView to the existing 3D Slicer Transform Visualizer across four attributes: helpfulness in comprehension, interpretability, intuitiveness, and user confidence, using Likert ratings and the System Usability Scale. On average, DeformView was rated higher across all categories (mean: 4.1/5.0 vs 3.2/5.0), with statistically significant improvements in helpfulness (p=0.008) and intuitiveness (p=0.027). Overall, 80% of participants preferred DeformView over the existing module, confirming the value of our visualization approach.

Next Steps

TODO:

  1. Colour Map and Legend Modifications
  1. Jacobian-Specific Visualization Controls
  1. User Interface and Readability Improvements
    • Adjust cursor text size for improved readability
    • Implement a full reset of default settings, not limited to window/level
    • Should colour be ‘color’ in the UI? Americanize.
  2. Stability and Bug Fixes

Illustrations

Displacement Magnitude Map
Voxel-wise magnitude of non-linear deformation between preoperative T2-FLAIR MRI and intraoperative tumour resection T2-FLAIR, from Case 50 of the ReMIND dataset. Warmer colours indicate larger tissue displacement.

Displacement magnitude map

Jacobian determinant magnitude map
Visual of the Jacobian map, where red indicates tissue expansion and blue is tissue compression, as a percentage. This is the same data as the above displacement magnitude example.

Image

User Study Results
Comparison of DeformView with the existing Transform Visualizer module (n=10) across four attributes: helpfulness, interpretability, intuitiveness, and user confidence (1–5 scale; higher scores indicate better performance). DeformView is rated higher across all categories, with significant improvements in helpfulness and intuitiveness.

User study results

Background and References

Miner, R. C. (2017). Image-guided neurosurgery. Journal of Medical Imaging and Radiation Sciences, 48(4), 328–335.

Abhari, K., Baxter, J. S., Chen, E. C., Khan, A. R., Peters, T. M., De Ribaupierre, S., & Eagleson, R. (2014). Training for planning tumour resection: augmented reality and human factors. IEEE Transactions on Biomedical Engineering, 62(6), 1466–1477.

King, F., Lasso, A., & Pinter, C. (2015, August 4). TransformVisualizer (Documentation/Nightly/Modules). 3D Slicer Wiki. Link

Vlachogianni, P., & Tselios, N. (2022). Perceived usability evaluation of educational technology using the System Usability Scale (SUS): A systematic review. Journal of Research on Technology in Education, 54(3), 392–409.

Drouin, S., Kochanowska, A., Kersten-Oertel, M., Gerard, I. J., Zelmann, R., De Nigris, D., … & Collins, D. L. (2017). IBIS: an OR ready open-source platform for image-guided neurosurgery. International Journal of Computer Assisted Radiology and Surgery, 12(3), 363–378.

Chung, M. K., Worsley, K. J., Paus, T., Cherif, C., Collins, D. L., Giedd, J. N., … & Evans, A. C. (2001). A unified statistical approach to deformation-based morphometry. NeuroImage, 14(3), 595–606.

Juvekar, P., Dorent, R., Kögl, F., Torio, E., Barr, C., Rigolo, L., … & Kapur, T. (2024). REMiND: The brain resection multimodal imaging database. Scientific Data, 11(1), 494.

Crameri, F., & Hason, S. (2024). Navigating color integrity in data visualization. Patterns, 5(5), 100972. doi:10.1016/j.patter.2024.100972