Continuing the Legacy of Grant’s Museum: Anatomy Education in the Digital Era
Notice bibliographique
Résumé
Introduction In the 1930s, an innovative adjunct available to anatomy learners was Grant’s Atlas of Anatomy and Grant’s Museum specimens of regional anatomy. With advancements in technology, learning tools used in anatomy education now also include digital reproductions of cadaveric specimens into 3D or interactive formats. Recent pivoting to online teaching and increased reliance on digital resources makes it a natural progression to advance the legacy of Grant's work with an online library of 3D models matching the museum. Realistic digital models are typically produced by either 3D reconstruction from image data, or photogrammetry, both requiring technical skill and time. Recent advances in light technology scanning equipment like the Artec Space Spider claim to produce a high‐fidelity 3D model within a time frame of 2‐3 hours from scan to finished digital model using their bundled software. In an ongoing project creating 3D models from prosections reproducing Grant’s Museum, we aimed to determine the efficiency of digital capture and the final model fidelity using the Artec Space Spider scanner. Methods Twenty‐seven prosections were created to replicate 38 figures in Grant’s Atlas, ranging from simple geometries such as a lung, to more complex, including the posterior abdominal wall. Scanning & post‐processing: Prosections were scanned using the Artec Space Spider, which captured images at around 7.5 frames per second (0.1 mm resolution). Post‐processing was completed in Artec Studio (Artec Studio 15 Professional x64 15.1.2.60) including alignment, hole‐filling and texture overlay. Early on, it was determined that many models needed digital sculpting, so we engaged expert digital artists using software (Zbrush 2021.6.6) for refinement of the models. The amount of time it took to scan and process each model was calculated to determine efficiency of model development. Fidelity confirmation: Ongoing feedback is being collected from an expert panel of anatomists representing five post‐secondary institutions. These experts are provided finished digital models and feedback is collected on the aspects of texture, lighting, anatomical detail (resolution) and model manipulation (user tools for examining online). This feedback will be finalized in February 2022. Results Table 1 depicts the breakdown of scanning and post‐processing time. Forty percent of the models were completed in under 3 hours, and these included prosections with simple geometries. Sixty percent of the models took more than 3 hours to complete, and these included more intricate prosections. Conclusions While the Artec Space Spider may be a great tool to quickly generate anatomical models with simple geometry or features, it may not be ideal for replicating intricate anatomical prosections. Only 40% of scans took less than 3 hours to complete with most moderately complex models and all complex models taking over 3 hours. Much of the time spent on these models was in post‐processing, which was dedicated to filling gaps or refining the details of the models. Our findings suggest that the time:cost ratio may not justify the purchase of such high‐level technology when photogrammetry may result in similar models for relatively less investment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».