Comparison of 3D reconstructive technologies used for morphometric research and the translation of knowledge using a decision matrix
Bibliographic record
Abstract
The use of three-dimensional (3D) models for education, pre-operative assessment, presurgical planning, and measurement have become more prevalent. With the increase in prevalence of 3D models there has also been an increase in 3D reconstructive software programs that are used to create these models. These software programs differ in reconstruction concepts, operating system requirements, user features, cost, and no one program has emerged as the standard. The purpose of this study was to conduct a systematic comparison of three widely available 3D reconstructive software programs, Amira(®), OsiriX, and Mimics(®) , with respect to the software's ability to be used in two broad themes: morphometric research and education to translate morphological knowledge. Cost, system requirements, and inherent features of each program were compared. A novel concept selection tool, a decision matrix, was used to objectify comparisons of usability of the interface, quality of the output, and efficiency of the tools. Findings indicate that Mimics was the best-suited program for construction of 3D anatomical models and morphometric analysis, but for creating a learning tool the results were less clear. OsiriX was very user-friendly; however, it had limited capabilities. Conversely, although Amira had endless potential and could create complex dynamic videos, it had a challenging interface. These results provide a resource for morphometric researchers and educators to assist the selection of appropriate reconstruction programs when starting a new 3D modeling project.
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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 | 0.029 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".