While a wide range of sophisticated methods is available, most segmentation software uses one or a combination of three basic techniques, threshold-based, edge-based, and region-growing, each of which should be understood when assessing the software. 7, 10, 13 Image segmentation is not straightforward, being complicated by factors such as low or overlapping contrast of the object of interest with other areas of the scan, irregular boundaries, partial-volume effects, noise, and motion, to name a few. Segmentation is the task of partitioning the image into non-overlapping, constituent regions, usually within a defined pixel intensity range. 1, beginning with importing a stack of 2D images from a CT or MRI scan and ending with a file suitable for rendered viewing, 3D printing, or finite element meshing and analysis. 19, 20, 9 16Ī typical process of segmenting a 3D image is illustrated in Fig. 5 Patient-specific, 3D printed models are also well suited for diagnosis and treatment planning for congenital heart disease, helping to clarify the complicated anatomy of the heart, great vessels, and coronary arteries before intervention in a wide range of defects. The applications of 3D printed models in orthopedics are predicted to grow exponentially in the coming years. 5, 1, 2 Commercial enterprises have developed to offer customized hip, shoulder, and cranio-maxillofacial replacements (e.g., KLS Martin WORLD, Tuttlingen, Germany LOGEEKs Medical Systems, Novosibirsk, Russia Materialise, Leuwen, Belgium). For example, printed models are especially useful in orthopedics for understanding the anatomy of bones and joints, manufacturing customized orthotics and implants, surgical planning, customized jigs, addressing deformities, teaching, and research. The applications and benefits of these patient-specific, printed models are many. The clinical and industry trend toward 3D viewing of reconstructed images also makes possible creation of 3D printed models from additive manufacturing (AM). In this study, we evaluate the suitability of several commercial packages for use in an introductory learning module intended to introduce BME students to image segmentation, share a description of the developed learning module, and present results from classroom implementation in multiple course settings. Acquisition of these skills is important to make BME undergraduate students more marketable for a variety of professional development opportunities, including summer internships, graduate school, and industry jobs, and would also prove useful in their curriculum for tasks such as obtaining 3D anatomy for design projects or engineering analysis. With recent advances in three-dimensional (3D) digital imaging technology, the need for biomedical engineering (BME) students to learn the basics of extracting specific anatomical features from the images, a process called segmentation, has grown significantly. 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.Ĭlinical practice has long used medical images for diagnosis and treatment planning. 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. 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. This module was implemented in three different engineering courses, impacting more than 150 students, and student achievement of learning goals was assessed. 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. 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. 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.
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