An Introductory Module in Medical Image Segmentation for BME Students

To support recent trends toward the use of patient-specific anatomical models from medical imaging data, we present a learning module for use in the undergraduate BME curriculum that introduces image segmentation, the process of partitioning digital images to isolate specific anatomical features. Five commercially available software packages were evaluated based on their perceived learning curve, ease of use, tools for segmentation and rendering, special tools, and cost: ITK-SNAP, 3D Slicer, OsiriX, Mimics, and Amira. After selecting the package best suited for a stand-alone course module on medical image segmentation, instructional materials were developed that included a general introduction to imaging, a tutorial guiding students through a step-by-step process to extract a skull from a provided stack of CT images, and a culminating assignment where students extract a different body part from clinical imaging data. This module was implemented in three different engineering courses, impacting more than 150 students, and student achievement of learning goals was assessed. ITK-SNAP was identified as the best software package for this application because it is free, easiest to learn, and includes a powerful, semi-automated segmentation tool. After completing the developed module based on ITK-SNAP, all students attained sufficient mastery of the image segmentation process to independently apply the technique to extract a new body part from clinical imaging data. This stand-alone module provides a low-cost, flexible way to bring the clinical and industry trends combining medical image segmentation, CAD, and 3D printing into the undergraduate BME curriculum.