3D Printing of Digitally Traced Neurons for Neuroanatomy Education
Notice bibliographique
Résumé
Purpose Three dimensional (3D) printing technology is being increasingly utilized in anatomy education, and its efficacy as a teaching tool is a growing area of education research. Most curriculum guides for 3D printing activities have students download pre‐designed digital anatomical 3D object files from websites (i.e. www.thingiverse.comor www.neuromorpho.org ) rather than generate their own 3D models from original data. Recently a technique was published for tracing and 3D‐printing neurons, but it required expensive software, as well as extensive computer programing experience (McDougal and Shepard, 2015). The goal of our study was to develop a simple, intuitive, and inexpensive method to allow students to generate an original reconstructed neuron suitable for 3D‐printing, and to evaluate this method among targeted learners. Methods Neurons from mouse brain and retina were labeled with fluorescent dye, fixed and imaged sequentially in 1 μm sections with a Zeiss LSM laser scanning microscope. The image stack was loaded into ImageJ, traced, and morphologically analyzed using the free ImageJ plugin Simple Neurite Tracer (SNT). Traced neurons were then exported as an object file into Blender, a free 3D graphics software program, edited, and printed with PLA, ABA, and gypsum powder material, in various sizes and color patterns. Based on this work, a “3D Printing Neurons Made Easy” instruction guide and 5‐part tutorial video was crafted; the raw neuron image stack was published in The Cell Image Library , a freely accessible online public repository that students can access from any computer and trace to create their own digital 3D model. The curriculum was piloted in a neuroanatomy laboratory session for high school students in a summer neuroscience outreach program. Students were given a pre‐ and post‐test on topics related to microscopy, neuroanatomy and image analysis, as well as an exit survey. Results Our “3D Printing Neurons Made Easy” curriculum guide enabled students to participate in an optional, self‐guided activity to learn principles in confocal microscopy, neuroanatomy, and image analysis. By using open source software (Image J and Blender) and a public repository, this self‐guided activity engages students in neuroanatomy research at a relatively low cost. 70% students attempted (31/44) and of the 31 students that attempted the project, 87% of the students successfully printed a 3D neuron. 97% of these students had a positive experience with the activity and 80% agreed that they would recommend the activity to their high school biology class. The curriculum is now being piloted among undergraduate and graduate anatomy students, and analysis is ongoing. Conclusions We have developed an easy method for students to reconstruct z‐stack images of neurons for 3D printing. These 3D models provide dramatic examples of the complex structure of neurons, and may help communicate complex 3‐dimensional concepts to students. The activity also allows students with no prior research experience to learn principles in microscopy and 3D image analysis in a way that is fun, engaging, and low cost. Our “3D Printing Neurons Made Easy” instructions guide and tutorial videos may be useful for crowd‐sourcing data collection, anatomy education, training students in anatomical research methods, or attracting students to careers in research.
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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 | 0,000 | 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 | 0,000 |
| 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 ».