Perceptual organisation in diffusion MRI: curves and streamline flows
Bibliographic record
Abstract
This thesis proposes a new computational framework for the modeling of biological tissue structure in diffusion MRI data. By measuring the local Brownian motion of water molecules, diffusion MRI provides estimates of local fibre orientations in tissues such as the white matter of the brain. Over the last years, diffusion MRI has become an important tool for the in vivo study of brain connectivity. Nevertheless, the inference of the structure of white matter fibres is still an open problem. The methodology introduced in this thesis is based on differential geometry and perceptual organisation. The key ideas are to model white matter fibres as 3D space curves, to view diffusion MRI data as providing information about the tangent vectors of these curves, and to frame the problem as that of inferring 3D curve geometry from a discretized, incomplete, and potentially blurred and noisy field of tangent measurements. Inspired by notions used in perceptual organisation in computer vision, we develop local geometric constraints which guide the inference process and ultimately result in the recovery of the underlying fibre geometry. We start by introducing a notion of co-helicity between triplets of orientation estimates, which is incorporated in a geometric inference process. This process is referred to as 3D curve inference, and it estimates the parameters of the local best-fit osculating helix for each orientation in the dataset. Based on this, a relaxation labeling framework is set-up for the regularization of diffusion MRI data. We then develop a 3D curve inference technique for the identification of complex sub-voxel fibre configurations in high angular resolution diffusion
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".