3D Printing of Digitally Traced Neurons for Neuroanatomy Education
Bibliographic record
Abstract
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".