DICOM segmentation and STL creation for 3D printing: a process and software package comparison for osseous anatomy

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Abstract

Background

Extracting and three-dimensional (3D) printing an organ in a region of interest in DICOM images typically calls for segmentation as a first step in support of 3D printing. The DICOM images are not exported to STL data immediately, but segmentation masks are exported to STL models. After primary and secondary processing, including noise removal and hole correction, the STL data can be 3D printed. The quality of the 3D model is directly related to the quality of the STL data. This study focuses and reports on the DICOM to STL segmentation performance for nine software packages.

Methods

Multidetector row CT scanning was performed on a dry human mandible with two 10-mm-diameter bearing balls as a phantom. The DICOM image file was then segmented and exported to an STL file using nine different commercial/open-source software packages. Once the STL models were created, the data (file) properties and the size and volume of each file were measured, and differences across the software packages were noted. Additionally, to evaluate differences between the shapes of the STL models by software package, each pair of STL models was superimposed, with the observed differences between their shapes characterized as the shape error.

Results: The data (file) size of the STL file and the number of triangles that constitute each STL model were different across all software packages, but no statistically significant differences were found across software packages. The created ball STL model expanded in the X-, Y-, and Z-axis directions, with the length in the Z-axis direction (body axis direction) being slightly longer than that in the other directions. The mean shape error between software packages of the mandibular STL model was 0.11 mm, but there was no statistically significant difference between them.

Conclusions

Our results revealed that there are some differences between the software packages that perform the segmentation and STL creation of the DICOM image data. In particular, the features of each software package appeared in the fine and thin areas of the osseous structures. When using these software packages, it is necessary to understand the characteristics of each.

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Medical 3D Printing for the Radiologist.

Dimitris Mitsouras, Peter Liacouras, Amir Imanzadeh … (2015)

While use of advanced visualization in radiology is instrumental in diagnosis and communication with referring clinicians, there is an unmet need to render Digital Imaging and Communications in Medicine (DICOM) images as three-dimensional (3D) printed models capable of providing both tactile feedback and tangible depth information about anatomic and pathologic states. Three-dimensional printed models, already entrenched in the nonmedical sciences, are rapidly being embraced in medicine as well as in the lay community. Incorporating 3D printing from images generated and interpreted by radiologists presents particular challenges, including training, materials and equipment, and guidelines. The overall costs of a 3D printing laboratory must be balanced by the clinical benefits. It is expected that the number of 3D-printed models generated from DICOM images for planning interventions and fabricating implants will grow exponentially. Radiologists should at a minimum be familiar with 3D printing as it relates to their field, including types of 3D printing technologies and materials used to create 3D-printed anatomic models, published applications of models to date, and clinical benefits in radiology. Online supplemental material is available for this article.

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Additively manufactured medical products – the FDA perspective

Matthew Di Prima, James Coburn, David Hwang … (2015)

Additive manufacturing/3D printing of medical devices is becoming more commonplace, a 3D printed drug is now commercially available, and bioprinting is poised to transition from laboratory to market. Despite the variety of technologies enabling these products, the US Food and Drug Administration (FDA) is charged with protecting and promoting the public health by ensuring these products are safe and effective. To that end, we are presenting the FDA’s current perspective on additive manufacturing/3D printing of medical products ranging from those regulated by the Center for Devices and Radiological Health (CDRH), the Center for Drug Evaluation and Research (CDER), and the Center for Biologics Evaluation and Research (CBER). Each Center presents an overview of the additively manufactured products in their area and the specific concerns and thoughts on using this technology in those product spaces.

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Anatomic modeling using 3D printing: quality assurance and optimization

Shuai Leng, Kiaran McGee, Jonathan Morris … (2017)

Background The purpose of this study is to provide a framework for the development of a quality assurance (QA) program for use in medical 3D printing applications. An interdisciplinary QA team was built with expertise from all aspects of 3D printing. A systematic QA approach was established to assess the accuracy and precision of each step during the 3D printing process, including: image data acquisition, segmentation and processing, and 3D printing and cleaning. Validation of printed models was performed by qualitative inspection and quantitative measurement. The latter was achieved by scanning the printed model with a high resolution CT scanner to obtain images of the printed model, which were registered to the original patient images and the distance between them was calculated on a point-by-point basis. Results A phantom-based QA process, with two QA phantoms, was also developed. The phantoms went through the same 3D printing process as that of the patient models to generate printed QA models. Physical measurement, fit tests, and image based measurements were performed to compare the printed 3D model to the original QA phantom, with its known size and shape, providing an end-to-end assessment of errors involved in the complete 3D printing process. Measured differences between the printed model and the original QA phantom ranged from -0.32 mm to 0.13 mm for the line pair pattern. For a radial-ulna patient model, the mean distance between the original data set and the scanned printed model was -0.12 mm (ranging from -0.57 to 0.34 mm), with a standard deviation of 0.17 mm. Conclusions A comprehensive QA process from image acquisition to completed model has been developed. Such a program is essential to ensure the required accuracy of 3D printed models for medical applications.

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Author and article information

Contributors

Takashi Kamio:

ORCID: http://orcid.org/0000-0002-2559-7443

kamio@tky.ndu.ac.jp

Madoka Suzuki: madoka-a@tky.ndu.ac.jp

Rieko Asaumi: asaumi-r@tky.ndu.ac.jp

Taisuke Kawai: t-kawai@tky.ndu.ac.jp

Journal

Journal ID (nlm-ta): 3D Print Med

Journal ID (iso-abbrev): 3D Print Med

Title: 3D Printing in Medicine

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 2365-6271

Publication date (Electronic): 31 July 2020

Publication date PMC-release: 31 July 2020

Publication date Collection: December 2020

Volume: 6

Electronic Location Identifier: 17

Affiliations

GRID grid.412196.9, ISNI 0000 0001 2293 6406, Department of Oral and Maxillofacial Radiology, , The Nippon Dental University, ; 1-9-20 Fujimi-cho, Chiyoda-ku, Tokyo, 102-8159 Japan

Author information

Takashi Kamio http://orcid.org/0000-0002-2559-7443

Article

Publisher ID: 69

DOI: 10.1186/s41205-020-00069-2

PMC ID: 7393875

PubMed ID: 32737703

SO-VID: 217881b1-9359-40d3-8f65-a4ed6ff4ba3d

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 2 March 2020

Date accepted : 10 July 2020

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Keywords: 3d printing,computed-aided design,dicom image,fdm 3d printer,oral and maxillofacial surgery,patient-specific,stl file

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Keywords: 3d printing, computed-aided design, dicom image, fdm 3d printer, oral and maxillofacial surgery, patient-specific, stl file

DICOM segmentation and STL creation for 3D printing: a process and software package comparison for osseous anatomy

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Most cited references 21

Medical 3D Printing for the Radiologist.

Additively manufactured medical products – the FDA perspective

Anatomic modeling using 3D printing: quality assurance and optimization

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