Using NURBS for virtual resections in liver surgery planning: a comparative usability study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate. METHODS: The study compares the use of NURBS and B´ezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and B´ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively balancing complete tumor removal with the preservation of enough healthy liver tissue and function to prevent postoperative liver dysfunction, despite working with fewer control points and different surface properties. Accuracy was assessed using Hausdorff distance and average surface distance. A survey based on the NASA Task Load Index measured user performance and preferences. RESULTS: NURBS surfaces exhibit improved accuracy and consistency over B´ezier surfaces, with lower average surface distance and variability of results. The 95th percentile Hausdorff Distance indicates the robustness of NURBS surfaces for the task. Task completion time was influenced by control point dimensions, favoring NURBS 3x3 (vs. 4x4) surfaces for a balanced accuracy-efficiency trade-off. Finally, the survey results indicated participants preferred NURBS surfaces over B´ezier, emphasizing the improved performance, surface manipulation, and reduced effort. CONCLUSION: The integration of NURBS surfaces into liver resection planning offers a promising advancement. This study demonstrates their superiority in accuracy, efficiency, and user preference compared to B´ezier surfaces. The findings underscore the potential of NURBS-based preoperative planning tools to enhance surgical outcomes in liver resection procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it