Dimensional Error in Rapid Prototyping with Open Source Software and Low-cost 3D-printer

Rendón-Medina, Marco Aurelio; Delgado, Laura Andrade; Telich-Tarriba, José E; Fuente-Del-Campo, Antonio; Altamirano-Arcos, Carlos

doi:10.1097/gox.0000000000001646

Cited by 16 publications

(19 citation statements)

References 31 publications

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“…Many studies have focused on dry bone models as their gold standard, but it has been established through this and other studies [60] that partial volume effects related to voxel size are a major cause of segmentation variation when soft tissue segmentation is necessary. Multiple prior studies have investigated the variations in printed anatomical models [10,32,55,59], however, dry bone, ground truth studies cannot always account for the variability in clinical image volumes.…”

Section: Risk Mitigationmentioning

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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Section: Risk Mitigationmentioning

confidence: 99%

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

2022

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“…A precisão dimensional dos guias cirúrgicos obtidos por CAD/CAM e prototipagem rápida apresentou erro dimensional absoluto dentro dos padrões clinicamente 34 aceitos, com valores bastante acurados e precisos (SHAHEEN et al, 2017;SHIROTA et al, 2019XU et al, 2020), não havendo diferenças estatisticamente significativas entre os métodos convencional e virtual, após período entre um e dois anos (XU et al, 2020). Ao se comparar medidas lineares em uma mandíbula (modelo ex vivo) e em seu respectivo biomodelo, obtidos pela tecnologia CAD/CAM, utilizando softwares gratuitos, também não foram verificadas diferenças estatisticamente significativas (RENDÓN-MEDINA et al, 2018).…”

Section: Discussionunclassified

Ciências da Saúde: desafios, perspectivas e possibilidades - Volume 1

2021

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INTRODUÇÃO:A anestesiologia proporciona ausência ou alívio da dor e outras sensações ao paciente que necessita realizar procedimentos médicos e que vem ampliando sua área de atuação não só no intra-operatório mas também no pré e pós-operatório.Recentemente, foi implantado o consultório de pré-anestesico como item obrigatório para que sejam realizadas cirurgias. A presente pesquisa avaliou o grau de satisfação com o consultório de anestesia de uma amostra da população submetida a um procedimento cirúrgico, verificando a importância do anestesiologista e a confiança da comunidade após o contato com esse profissional no que tange o esclarecimento de dúvidas e a diminuição dos sintomas de ansiedade. MATERIAL E MÉTODOS: Aplicamos um questionário aos pacientes após a consulta pré-anestésica através de plataforma online Google Forms, indagando sobre seu grau de satisfação, a necessidade da avaliação, orientação que o mesmo desconhecia, entre outros. RESULTADOS: 86,8% receberam orientação pré-anestésica e 13,2% não receberam 76,3% receberam as orientações pelo anestesiologista, 23,7% receberam orientações de outro especialista. 94,7% dos pacientes acharam importante o contato prévio com o anestesista, 18,5% apresentavam sintomas de ansiedade anteriormente à consulta, e após a avaliação houve diminuição dos sintomas em 71,5% dos pacientes que antes possuíam. CONCLUSÃO: A avaliação pré-anestésica demonstrou reduzir níveis de ansiedade e dúvidas sobre o procedimento anestésico dos pacientes, porém tal avaliação não é tão divulgada, visto que 57,9% dos participantes desconheciam o que envolve a avaliação pré-anestésica, porém quando realizada, foi considerada relevante pelos pacientes esse contato prévio com o especialista.

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“…Many studies have focused on dry bone models as their gold standard, but it has been established through this and other studies [33] that partial volume effects related to voxel size are a major cause of segmentation variation when soft tissue segmentation is necessary. Dry bone ground truth studies do not always translate to actual clinical results.…”

Section: Risk Mitigationmentioning

confidence: 99%

Assessing Variability in Segmentation Algorithms for 3D Printing at the Point of Care

Fogarasi

Coburn

Ripley

2021

Preprint

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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 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 the 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.

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Dimensional Error in Rapid Prototyping with Open Source Software and Low-cost 3D-printer

Cited by 16 publications

References 31 publications

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

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

Ciências da Saúde: desafios, perspectivas e possibilidades - Volume 1

Assessing Variability in Segmentation Algorithms for 3D Printing at the Point of Care

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