Converting DICOM to STL for 3D Printing: A Process, and Software Package Comparison

Abstract Background : Extracting and three-dimensional (3D) printing an organ in a region of interest in DICOM images typically calls for segmentation in support of 3D printing as a first step. Next, the DICOM images are converted to STL data. 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 conversion performance for nine software packages. Methods : Multi-detector row CT scanning was performed on a dry human mandible with two 10-mm-diameter bearing balls as a phantom. The DICOM images file was then converted to a STL file using nine different commercial/open-source software packages. Once the STL models were constructed, the data properties and the size and volume of each 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 observed differences between their shapes characterized as shape error. Further, deformation caused by reduction in the number of triangles was evaluated. Results : The data size and the number of triangles were different across all software packages. The constructed 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 other directions. There were no significant differences in shape error across software packages for the mandible STL model. No shape change was observed relative to reduction in the number of triangles. Conclusions : Statistically, no significant differences were found across software packages for size and volume. However, different characteristics of each software package were noticeable, such as different effects in the thin cortical bone area, likely due to the partial volume effect, which may reflect differences in image binarization algorithms. Although the shape of the STL model differs slightly depending on the software, our results indicate that shape error in 3D printing for clinical use in oral and maxillofacial surgery remains within acceptable limits.