Incorporating 3D Printing Into Your Practice: Lessons Learned

Sharma, Akhil; Kirkland, Kyle S; Holloway, Robert M; Parekh, Selene G.

doi:10.1177/1938640020980912

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Many studies have identified absolute differences in accuracy between programs for specific software, [15,18], described 3D printing workflows for different applications [19][20][21], and discussed printing best practices [22,23]. In this paper, the behaviors of different mathematical algorithms used in ROI segmentation and refinement are reviewed to highlight key features that can affect the transformation of medical images into 3D printable models.…”

Section: Introductionmentioning

confidence: 99%

Algorithms used in medical image segmentation for 3D printing and how to understand and quantify their performance

2022

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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 models. The 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 refinement. Parameters for each that can introduce geometric variability are also identified. Several metrics for measuring variability between models and validating processes are explored and assessed. Methods Using a clinical maxillofacial CT scan of a patient with a tumor of the mandible, four segmentation and refinement workflows were processed using four software packages. Differences in segmentation were calculated using several techniques including volumetric, surface, linear, global, and local measurements. Results Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Volumetric intersections and heatmaps provided useful local metrics of mismatch or variance between models made by different workflows. They also allowed calculations of aggregate percentage agreement and disagreement which provided a global benchmark metric. For the relevant regions of interest (ROIs), statistically significant differences were found in the volume and surface area comparisons for the final mandible and tumor models, as well as between measurements of the nerve central path. 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 from differences in segmentation and refinement algorithms can be introduced into patient-specific models. No single metric was able to capture the true accuracy of the final models. However, a combination of global and local measurements provided an understanding of important geometric variations. The clinical implications of each geometric variation is different for each anatomical location and should be evaluated on a case-by-case basis by clinicians familiar with the process. Understanding the basic segmentation and refinement functions of software is essential for sites to create a baseline from which to evaluate their standard workflows, user training, and inter-user variability when using patient-specific models for clinical interventions or decisions.

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