Localizing 3-D Anatomical Landmarks Using Deep Convolutional Neural Networks
Bibliographic record
Abstract
Anatomical landmarks on 3-D human body scans play key roles in shape-essential applications, including consistent parameterization, body measurement extraction, segmentation, and mesh re-targeting. Manually locating landmarks is tedious and time-consuming for large-scale 3-D anthropometric surveys. To automate the landmarking process, we propose a data-driven approach, which learns from landmark locations known on a dataset of 3-D scans and predicts their locations on new scans. More specifically, we adopt a coarse-to-fine approach by training a deep regression neural network to compute the locations of all landmarks and then for each landmark training an individual deep classification neural network to improve its accuracy. In regards to input images being fed into the neural networks, we compute from a frontal view three types of image renderings for comparison, i.e., gray-scale appearance images, range depth images, and curvature mapped images. Among these, curvature mapped images result in the best empirical accuracy from the deep regression network, whereas depth images lead to higher accuracy for locating most landmarks using the deep classification networks. In conclusion, the proposed approach performs better than state of the art on locating most landmarks. The simple yet effective approach can be extended to automatically locate landmarks in large scale 3-D scan datasets.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".