End-To-End Vertebra Localization and Level Detection in Weakly Labelled 3D Spinal Mr using Cascaded Neural Networks
Bibliographic record
Abstract
Localization and identification of vertebrae in 3D MR volumes is a crucial first step for diagnosis and management of spinal conditions. Automating this process can save radiologists significant time and clicks. In this paper, we propose a novel learning-based approach consisting of two cascaded networks that perform simultaneous identification and localization of vertebrae. The first network performs slice-based level detection of full 3D sagittal volumes using an adaptive loss function that adjusts the weights of its loss terms during training, and outputs estimated center slices of each vertebrae. The sagittal slice is then divided into sub-volumes each containing a single vertebra. These sub-volumes are inputted into the second network for binary classification and localization of the vertebrae. Our method only requires centroid annotation (performed manually), a statistical model then provides an approximation of the volumetric segmentation for ground truth data. With this method, a vertebra identification rate of 82% was achieved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".