A Comparative Study of Segmentation Models for the Identification of the Trapezium Bone in X-rays
Bibliographic record
Abstract
Recent advancements in deep learning have rendered the identification of bones in X-ray images imperative for tasks such as anomaly detection and surgical procedures.However, the presence of overlapping bones, such as the trapezium, has the potential to compromise the efficacy of this identification.Segmenting the trapezium in X-ray images poses a significant challenge due to its overlap with surrounding bones, including the scaphoid and trapezoid.This study explores the use of deep learning techniques to assist surgeons in accurately localizing the trapezium bone in X-ray images of the hand.This can be helpful in surgical procedures such as trapeziometacarpal joint replacement surgery.The efficacy of a set of models (namely SAM, Mobile-SAM and U-Net) was tested by utilizing radiographic images.Furthermore, a hybrid approach integrating object detection and segmentation was developed.Initially, the object detection model YOLOv8 was trained to localize the region of the image containing the trapezium.This model demonstrated a high level of performance in identifying the trapezium.The utilization of the segmentation model, U-Net, resulted in the identification of pixels belonging to the trapezium bone, thereby achieving a Dice score of 94%.This two-step approach underscores the benefits of this algorithm by reducing the computational load while maintaining high performance.
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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 | 0.001 |
| 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 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".