Assessing Variability in Segmentation Algorithms for 3D Printing at the Point of Care
Abstract Background: 3D Printing (3DP) has enabled medical professionals to create patient specific medical devices to assist in surgical planning. Anatomical models can be generated from patient scans using a wide array of software, but there are limited studies on the geometric variance that is introduced during the digital conversion of images to model. Final accuracy of the 3D printed model is a function of manufacturing hardware quality control and the variability introduced during the multiple digital steps that convert patient scans to a printable format. This study provides a brief summary of common algorithms used for segmentation and their principal features. We also identify critical parameters and steps in the workflow where geometric variation may be introduced. We then provide suggested methods to measure or reduce the variation and mitigate these risks. Methods: Using a clinical head CT scan of a mandible containing a tumor, we performed segmentations in four separate programs using workflows optimized for each. Differences in segmentation were calculated using several techniques.Results: Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Residual volumes were calculated to generate pairwise agreement and disagreement percentages between each as program’s model. For the relevant ROIs, statistically significant differences were found globally in the volume and surface area comparisons between final bone and tumor models, as well locally between nerve centroid measurements – major variance introduced due to workflow is highlighted in difference heat maps. As with all clinical use cases, statistically significant results must be weighed against the clinical significance of any deviations found. Conclusions: Statistically significant geometric variations can be introduced to patient specific models from differences in software applications. The global and local variations should be evaluated for a full understanding of geometric variations. The clinical implications of these variations vary by anatomical location and should be evaluated on a case-by-case basis by certified clinicians. Understanding the basic functions of segmentation and 3D print preparation software is essential for users intending to adopt the use of patient specific models for clinical intervention or decision making.