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The reconstruction of two-dimensional (2D) slices to three-dimensional (3D) digital anatomical models requires technical skills and software that are becoming increasingly important to the modern anatomist, but these skills are rarely taught in undergraduate science classrooms. Furthermore, learning opportunities that allow students to simultaneously explore anatomy in both 2D and 3D space are increasingly valuable. This report describes a novel learning activity that trains students to digitally trace a serially imaged neuron from a confocal stack and to model that neuron in 3D space for 3D printing. By engaging students in the production of a 3D digital model, this learning activity is designed to provide students a novel way to enhance their understanding of the content, including didactic knowledge of neuron morphology, technical research skills in image analysis, and career exploration of neuroanatomy research. Moreover, students engage with microanatomy in a way that starts in 2D but results in a 3D object they can see, touch, and keep. This discursive article presents the learning activity, including videos, instructional guides, and learning objectives designed to engage students on all six levels of Bloom's Taxonomy. Furthermore, this work is a proof of principle modeling workflow that is approachable, inexpensive, achievable, and adaptable to cell types in other organ systems. This work is designed to motivate the expansion of 3D printing technology into microanatomy and neuroanatomy education. © 2024 The Authors. Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.

Citation

Jordan M Renna, Katelyn B Sondereker, Christopher L Cors, Sara N Chaszeyka, Kristin N Keenan, Michael R Corigliano, Lindsey A Milgrom, Jessica R Onyak, Edward J Hamad, Maureen E Stabio. From 2D slices to a 3D model: Training students in digital microanatomy analysis techniques through a 3D printed neuron project. Anatomical sciences education. 2024 Apr-May;17(3):499-505

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PMID: 38379173

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