Accelerated needle steering using partitioned value iteration
Bibliographic record
Abstract
This paper presents a fast 2D motion planner for steering flexible needles inside relatively rigid tissue. This approach exploits a nonholonomic system approach, which models tissue-needle interaction, and formulates the problem as a Markov Decision Process that is solvable using infinite horizon Dynamic Programming. Starting from any initial condition defined in the workspace, this method calculates a set of control actions that enables the needle to reach the target and avoid collisions with obstacles. Unlike conventional solvers, e.g. the value iterator, which suffers from the curse of dimensionality, partitioned-based solvers show promising numerical performance. Given a segmented image of a workspace including the locations of the obstacles, the target and the entry point, the partitioned-based solver provides a descent solution where high resolution is required. It is shown in this paper how prioritized partitioning increases computational performance of the current DP-based solutions for the purpose of off-line path planning. By default, our planner selects the path with the least number of turning points while maintaining minimum insertion length, which leads to the least damage to tissue. In this paper, more emphasis is given to the control aspects of the problem rather than the corresponding biomedical issues.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".