A workflow demonstration of patient-specific anatomical model fabrication using additive manufacturing

¹ Assistant Professor, Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, 413006 Maharashtra, India
^2,3,4 Student, Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, 413006 Maharashtra, India.
⁵ Professor, Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, 413006 Maharashtra, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 10 March 2026

Abstract

This research presents an exploratory study on the integration of additive manufacturing (AM) with medical imaging for the fabrication of anatomical models. The study focuses on the conversion of DICOM (Digital Imaging and Communications in Medicine) data, obtained from computed tomography (CT) scans, into 3D-printable STL files. Rather than developing functional prosthetics, the objective is to create high- fidelity, non-functional anatomical models for use in education, training, and research. Medical imaging data provided by healthcare professionals are processed using specialized segmentation and 3D modeling software to extract and reconstruct targeted anatomical structures. These reconstructions are exported as STL files and fabricated using 3D printing techniques. Key considerations such as image resolution, model scaling, segmentation accuracy, and suitable material selection are addressed to ensure model precision and printability. The proposed workflow establishes a seamless link between diagnostic imaging and physical model creation, demonstrating the potential of AM in medical visualization, pre-surgical planning, and patient communication. This study serves as a proof-of-concept, emphasizing the feasibility and benefits of converting CT-derived imaging data into tangible models. It highlights the growing role of additive manufacturing in healthcare innovation, particularly in improving anatomical comprehension and bridging the gap between digital imaging and physical representation.