Assessing Variability in Segmentation Algorithms for 3D Printing at the Point of Care (original) (raw)
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3D printing in medicine, 2022
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.
Factors Affecting Dimensional Accuracy of 3-D Printed Anatomical Structures Derived from CT Data
Journal of Digital Imaging, 2015
Additive manufacturing and bio-printing, with the potential for direct fabrication of complex patient-specific anatomies derived from medical scan data, are having an ever-increasing impact on the practice of medicine. Anatomic structures are typically derived from CT or MRI scans, and there are multiple steps in the model derivation process that influence the geometric accuracy of the printed constructs. In this work, we compare the dimensional accuracy of 3-D printed constructs of an L1 vertebra derived from CT data for an ex vivo cadaver T-L spine with the original vertebra. Processing of segmented structures using binary median filters and various surface extraction algorithms is evaluated for the effect on model dimensions. We investigate the effects of changing CT reconstruction kernels by scanning simple geometric objects and measuring the impact on the derived model dimensions. We also investigate if there are significant differences between physical and virtual model measurements. The 3-D models were printed using a commercial 3-D printer, the Replicator 2 (MakerBot, Brooklyn, NY) using polylactic acid (PLA) filament. We found that changing parameters during the scan reconstruction, segmentation, filtering, and surface extraction steps will have an effect on the dimensions of the final model. These effects need to be quantified for specific situations that rely on the accuracy of 3-D printed models used in medicine or tissue engineering applications.
From medical imaging data to 3D printed anatomical models
PLOS ONE
Anatomical models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it possible to create costefficient patient-specific models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT)) to 3D printed physical models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an anatomical model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.
Frontiers in medical technology, 2023
3D printing enables the rapid manufacture of patient-specific anatomical models that substantially improve patient consultation and offer unprecedented opportunities for surgical planning and training. However, the multistep preparation process may inadvertently lead to inaccurate anatomical representations which may impact clinical decision making detrimentally. Here, we investigated the dimensional accuracy of patient-specific vascular anatomical models manufactured via digital anatomical segmentation and Fused-Deposition Modelling (FDM), Stereolithography (SLA), Selective Laser Sintering (SLS), and PolyJet 3D printing, respectively. All printing modalities reliably produced hand-held patient-specific models of high quality. Quantitative assessment revealed an overall dimensional error of 0.20 ± 3.23%, 0.53 ± 3.16%, −0.11 ± 2.81% and −0.72 ± 2.72% for FDM, SLA, PolyJet and SLS printed models, respectively, compared to unmodified Computed Tomography Angiograms (CTAs) data. Comparison of digital 3D models to CTA data revealed an average relative dimensional error of −0.83 ± 2.13% resulting from digital anatomical segmentation and processing. Therefore, dimensional error resulting from the print modality alone were 0.76 ± 2.88%, + 0.90 ± 2.26%, + 1.62 ± 2.20% and +0.88 ± 1.97%, for FDM, SLA, PolyJet and SLS printed models, respectively. Impact on absolute measurements of feature size were minimal and assessment of relative error showed a propensity for models to be marginally underestimated. This study revealed a high level of dimensional accuracy of 3D-printed patient-specific vascular anatomical models, suggesting they meet the requirements to be used as medical devices for clinical applications.
Applied System Innovation
The objective of the present study is to make a comparison between various free and open-source software used for medical image processing, such as 3D Slicer (version 4.11), ITK-Snap (version 3.8), and Invesalius (version 3.1) in its application for the calculation of solitary fibrous tumor volumes. Knowing the size, shape, and volume of mesothelioma is decisive for clinical decision-making by health personnel when performing surgery; the currently used standard procedure is manual segmentation through magnetic resonance imaging (MRI). This process tends to take a long time to complete. On the other hand, automatic segmentation software is much faster and more user-friendly, so looking for software that gives us greater accuracy when doing this task is very important. This work obtained magnetic resonance imaging (MRI) of a mesothelioma patient, and the images were segmented in the 3 different programs to evaluate the concordance between the software later.
Surgical Science, 2014
Background: Three-dimensional printing (3DP) technologies are a trendsetting topic, also in the field of surgery. Preoperative planning for maxillofacial and neurological surgery, for instance, increasingly involves skull models obtained by 3DP. However, the cranial replicas currently used in this context have been shown to not reproduce the exact anatomy of the individual patient undergoing surgery. Objective: The present study aimed at investigating the extent to which manual editing, using current computer software tools, can improve skull models derived from medical images. Methods: Skull computed tomography (CT) was obtained on three cadavers and sent to three institutions that provide preoperative 3DP services. Each institute independently performed 3D reconstructions, including routine manual editing, and subsequently produced the replicas. The models were then qualitatively compared with the respective original skull. For quantitative comparison surface scans of particular regions of interest were made and the deviations assessed using 3-matic software (Materialise, Leuven, Belgium). Results: Routine manual editing of CT images resulted in replicas that were clear improvements over automatically generated reconstructions. This was particularly the case for teeth artefacts and thin-walled entities (e.g. paranasal sinuses). Conversely, however, many anatomical structures remained incorrectly rendered (e.g. orbitae, pterygoid processes, and sella turcica). Extraosseous calcifications had regularly not been removed. After extensive manual editing, however, replicas were able to provide largely submillimeter accuracy (mean deviation 0.2496 mm; standard deviation ±0.2276 mm). Conclusions: This study confirms that manual editing with current computer science tools does improve the quality of CT-based 3D printed skull models. But, it also demonstrates that a number
The impact of manual threshold selection in medical additive manufacturing
International Journal of Computer Assisted Radiology and Surgery, 2016
Purpose Medical additive manufacturing requires standard tessellation language (STL) models. Such models are commonly derived from computed tomography (CT) images using thresholding. Threshold selection can be performed manually or automatically. The aim of this study was to assess the impact of manual and default threshold selection on the reliability and accuracy of skull STL models using different CT technologies. Method One female and one male human cadaver head were imaged using multi-detector row CT, dual-energy CT, and two cone-beam CT scanners. Four medical engineers manually thresholded the bony structures on all CT images. The lowest and highest selected mean threshold values and the default threshold value were used to generate skull STL models. Geometric variations between all manually thresholded STL models were calculated. Furthermore, in order to calculate the accuracy of the manually and default thresholded STL models, all STL models were superimposed on an optical scan of the dry female and male skulls ("gold standard"). Results The intra-and inter-observer variability of the manual threshold selection was good (intra-class correlation coefficients >0.9). All engineers selected grey values closer to soft tissue to compensate for bone voids. Geometric variations between the manually thresholded STL models were 0.13 mm (multi-detector row CT), 0.59 mm (dual-B Maureen van Eijnatten
Applied Sciences, 2020
Featured Application: Accuracy of additively manufactured implants for clinical surgery. Abstract: In craniomaxillofacial surgical procedures, an emerging practice adopts the preoperative virtual planning that uses medical imaging (computed tomography), 3D thresholding (segmentation), 3D modeling (digital design), and additive manufacturing (3D printing) for the procurement of an end-use implant. The objective of this case study was to evaluate the cumulative spatial inaccuracies arising from each step of the process chain when various computed tomography protocols and thresholding values were independently changed. A custom-made quality assurance instrument (Phantom) was used to evaluate the medical imaging error. A sus domesticus (domestic pig) head was analyzed to determine the 3D thresholding error. The 3D modeling error was estimated from the computer-aided design software. Finally, the end-use implant was used to evaluate the additive manufacturing error. The results were verified using accurate measurement instruments and techniques. A worst-case cumulative error of 1.7 mm (3.0%) was estimated for one boundary condition and 2.3 mm (4.1%) for two boundary conditions considering the maximum length (56.9 mm) of the end-use implant. Uncertainty from the clinical imaging to the end-use implant was 0.8 mm (1.4%). This study helps practitioners establish and corroborate surgical practices that are within the bounds of an appropriate accuracy for clinical treatment and restoration.
Methods for verification of 3D printed anatomic model accuracy using cardiac models as an example
3D Printing in Medicine
Background: Medical 3D printing has brought the manufacturing world closer to the patient's bedside than ever before. This requires hospitals and their personnel to update their quality assurance program to more appropriately accommodate the 3D printing fabrication process and the challenges that come along with it. Results: In this paper, we explored different methods for verifying the accuracy of a 3D printed anatomical model. Methods included physical measurements, digital photographic measurements, surface scanning, photogrammetry, and computed tomography (CT) scans. The details of each verification method, as well as their benefits and challenges, are discussed. Conclusion: There are multiple methods for model verification, each with benefits and drawbacks. The choice of which method to adopt into a quality assurance program is multifactorial and will depend on the type of 3D printed models being created, the training of personnel, and what resources are available within a 3D printed laboratory.
Geometrical accuracy evaluation of an affordable 3D printing technology for spine physical models
Journal of Clinical Neuroscience, 2020
The aim of the study is to develop a workflow to establish geometrical quality criteria for 3D printed anatomical models as a guidance for selecting the most suitable 3D printing technologies available in a clinical environment. Methods: We defined the 3D geometry of a 25-year-old male patient's L4 vertebra and the geometry was then printed using two technologies, which differ in printing resolution and affordability: Fused Deposition Modelling (FDM) and Digital Light Processing (DLP). In order to measure geometrical accuracy, the 3D scans of two physical models were compared to the virtual input model. To compare surface qualities of these printing technologies we determined surface roughness for two regions of interest. Finally, we present our experience in the clinical application of a physical model in a congenital deformity case. Results: The analysis of the distribution of the modified Hausdorff distance values along the vertebral surface meshes (99% of values <1 mm) of the 3D printed models provides evidence for high printing accuracy in both printing techniques. Our results demonstrate that the surface qualities, measured by roughness are adequate (~99% of values <0.1 mm) for both physical models. Finally, we implemented the FDM physical model for surgical planning. Conclusion: We present a workflow capable of determining the quality of 3D printed models and the application of a high quality and affordable 3D printed spine physical model in the pre operative planning. As a result of the visual guidance provided by the physical model, we were able to define the optimal trajectory of the screw insertion during surgery.