3D Knee Joint Modeling from MRI Images
Bibliographic record
Abstract
Introduction : Osteoarthritis is one of the major diseases that cause disability. Previous research studies implicate that the onset of osteoarthritis is associated with the changes in biomechanics of articular cartilages in the knee joint. In recent years, computer models have been extensively used to study the biomechanics of the cartilaginous tissues. One of the first steps of such studies is to construct anatomically accurate models of the tissues using available software packages. The objective of this study was to compare the capabilities of two software packages, Rhinoceros 3D and Mimics. Method : MRI data was collected from two volunteer subjects (one female age: 28 and one male age: 27). The subjects had no records of previous knee injuries or surgeries. In Rhinoceros 3D, the original MRI data was first imported to Sante DICOM Viewer and then exported as JPEG format. The exported images then were imported by Rhinoceros 3D for segmentation. Mimics software has a built-in tool to read MRI data directly. A tool called 3D LiveWire was used for segmenting. Finally, 3D surface models were obtained using segments. Results : Both Rhinoceros and Mimics have successfully obtained a model for femur. However, Rhinoceros has problems constructing other cartilaginous surfaces. Using Mimics, all other tissues of the knee joint were successfully constructed. A completed joint model included the following parts: Femur, Tibia, Fibula Femoral, Tibial and Fibular Cartilages Menisci Collateral and Cruciate Ligaments Patella and Patellar Cartilage Discussion : Rhinoceros 3D had problems constructing cartilages with complicated surfaces, but its open- programming feature allows it to integrate customized tools and scripts to help construct surfaces. Nevertheless, Mimics has many tools for segmentation and 3D model calculating. It also includes options to modify and optimize the models. In summary, the models from Mimics are qualified for future Finite Element studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".