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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.
Keywords
3D printing, Computed-aided design, DICOM Image, FDM 3D printer, Oral and maxillofacial surgery, Patient-specific, STL file
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CURRENT STATUS: Published
View the published article at 3D Printing in Medicine.
Version 4
Posted 04 Jun, 2020
No community comments so far
Editorial decision: Accept
On 08 Jul, 2020
Editor assigned
On 28 May, 2020
Submission checks complete
On 27 May, 2020
Editor invited
On 27 May, 2020
No comments provided
Editorial decision: Minor revision
On 28 May, 2020
Review #2 received
Received 26 May, 2020
Review #1 received
Received 20 May, 2020
Reviewer #1 agreed
On 12 May, 2020
Reviewer #2 agreed
On 12 May, 2020
Reviewers invited
Invitations sent on 09 May, 2020
Editor assigned
On 06 May, 2020
Submission checks complete
On 05 May, 2020
Editor invited
On 05 May, 2020
No comments provided
Editorial decision: Major revision
On 26 Apr, 2020
Review #2 received
Received 19 Apr, 2020
Review #1 received
Received 13 Apr, 2020
Reviewer #2 agreed
On 04 Apr, 2020
Reviewer #1 agreed
On 31 Mar, 2020
Reviewers invited
Invitations sent on 31 Mar, 2020
Editor assigned
On 24 Mar, 2020
Submission checks complete
On 23 Mar, 2020
Editor invited
On 23 Mar, 2020
No comments provided
Editorial decision: Revise before peer review
On 19 Mar, 2020
Editor assigned
On 03 Mar, 2020
First submitted
On 02 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
Metrics
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HTML Views: ...
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See the published version of this preprint at 3D Printing in Medicine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at 3D Printing in Medicine.
Version 4
Posted 04 Jun, 2020
No community comments so far
Editorial decision: Accept
On 08 Jul, 2020
Editor assigned
On 28 May, 2020
Submission checks complete
On 27 May, 2020
Editor invited
On 27 May, 2020
No comments provided
Editorial decision: Minor revision
On 28 May, 2020
Review #2 received
Received 26 May, 2020
Review #1 received
Received 20 May, 2020
Reviewer #1 agreed
On 12 May, 2020
Reviewer #2 agreed
On 12 May, 2020
Reviewers invited
Invitations sent on 09 May, 2020
Editor assigned
On 06 May, 2020
Submission checks complete
On 05 May, 2020
Editor invited
On 05 May, 2020
No comments provided
Editorial decision: Major revision
On 26 Apr, 2020
Review #2 received
Received 19 Apr, 2020
Review #1 received
Received 13 Apr, 2020
Reviewer #2 agreed
On 04 Apr, 2020
Reviewer #1 agreed
On 31 Mar, 2020
Reviewers invited
Invitations sent on 31 Mar, 2020
Editor assigned
On 24 Mar, 2020
Submission checks complete
On 23 Mar, 2020
Editor invited
On 23 Mar, 2020
No comments provided
Editorial decision: Revise before peer review
On 19 Mar, 2020
Editor assigned
On 03 Mar, 2020
First submitted
On 02 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at 3D Printing in Medicine.
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.
Figure 1
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Figure 4
Figure 5
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Figure 8
Figure 9
Figure 10
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