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      From medical imaging data to 3D printed anatomical models

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          Abstract

          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 cost-efficient 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.

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          Most cited references 14

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          Adaptive segmentation of MRI data.

          Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intrascan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intrascan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging (MRI) data, that has proven to be effective in a study that includes more than 1000 brain scans. Implementation and results are described for segmenting the brain in the following types of images: axial (dual-echo spin-echo), coronal [three dimensional Fourier transform (3-DFT) gradient-echo T1-weighted] all using a conventional head coil, and a sagittal section acquired using a surface coil. The accuracy of adaptive segmentation was found to be comparable with manual segmentation, and closer to manual segmentation than supervised multivariant classification while segmenting gray and white matter.
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            The application of rapid prototyping techniques in cranial reconstruction and preoperative planning in neurosurgery.

            The value of rapid prototype models of the skull in our craniofacial and neurosurgical practice was analyzed. Individual skull models of 52 patients were produced by means of rapid prototyping techniques and used in various procedures. Patients were divided into three groups as follows: group I (26 patients) requiring corrective cranioplasty 1) after resection of osseous tumors (15 patients) and 2) with congenital and posttraumatic craniofacial deformities (11 patients), group II (10 patients) requiring reconstructive cranioplasty, and group III (16 patients) requiring planning of difficult skull base approaches. The utility of the stereolithographic models was assessed using the Gillespie scoring system. The esthetic and clinical outcomes were assessed by means of the esthetic outcome score and the Glasgow Outcome Score, respectively. Simulation of osteotomies for advancement plasty and craniofacial reassembly in the model before surgery in group I reduced operating time and intraoperative errors. In group II, the usefulness of the models depended directly on the size and configuration of the cranial defect. The planning of approaches to uncommon and complex skull base tumors (group III) was significantly influenced by the stereolithographic models. The esthetic outcome was pleasing. The indications for the manufacture of individual three-dimensional models could be cases of craniofacial dysmorphism that require meticulous preoperative planning and skull base surgery with difficult anatomical and reconstructive problems. The stereolithographic models provide 1) better understanding of the anatomy, 2) presurgical simulation, 3) intraoperative accuracy in localization of lesions, 4) accurate fabrication of implants, and 5) improved education of trainees.
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              Simulation training in central venous catheter insertion: improved performance in clinical practice.

              To determine whether simulation training of ultrasound (US)-guided central venous catheter (CVC) insertion skills on a partial task trainer improves cannulation and insertion success rates in clinical practice. This prospective, randomized, controlled, single-blind study of first- and second-year residents occurred at a tertiary care teaching hospital from January 2007 to September 2008. The intervention group (n = 90) received a didactic and hands-on, competency-based simulation training course in US-guided CVC insertion, whereas the control group (n = 95) received training through a traditional, bedside apprenticeship model. Success at first cannulation and successful CVC insertion served as the primary outcomes. Secondary outcomes included reduction in technical errors and decreased mechanical complications. Blinded independent raters observed 495 CVC insertions by 115 residents over a 21-month period. Successful first cannulation occurred in 51% of the intervention group versus 37% of the control group (P = .03). CVC insertion success occurred for 78% of the intervention group versus 67% of the control group (P = .02). Simulation training was independently and significantly associated with success at first cannulation (odds ratio: 1.7; 95% confidence interval: 1.1-2.8) and with successful CVC insertion (odds ratio: 1.7; 95% confidence interval: 1.1-2.8)--both independent of US use, patient comorbidities, or resident specialty. No significant differences related to technical errors or mechanical complications existed between the two groups. Simulation training was associated with improved in-hospital performance of CVC insertion. Procedural simulation was associated with improved residents' skills and was more effective than traditional training.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 May 2017
                2017
                : 12
                : 5
                Affiliations
                [1 ]Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
                [2 ]Centre for Medical Imaging, University College London, London, United Kingdom
                University of Pennsylvania, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: DIN.

                • Data curation: TMB.

                • Funding acquisition: DIN TMB ERH JLR EM.

                • Investigation: TMB JLR AAP.

                • Methodology: JLR ERH AAP.

                • Project administration: DIN TMB ERH JLR.

                • Resources: AAP.

                • Supervision: DIN.

                • Validation: AAP.

                • Visualization: TMB ERH JLR.

                • Writing – original draft: TMB.

                • Writing – review & editing: DIN JLR AAP ERH EM.

                Article
                PONE-D-16-42523
                10.1371/journal.pone.0178540
                5451060
                28562693
                © 2017 Bücking et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 3, Pages: 10
                Product
                Funding
                Funded by: UCL Changemakers
                Award Recipient :
                University College London Changemakers ( https://www.ucl.ac.uk/changemakers) funded DIN, TMB, ERH, JLR, and EM. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
                Categories
                Research Article
                Engineering and technology
                Electronics engineering
                3D printing
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Ribs
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Ribs
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Computed Axial Tomography
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Computed Axial Tomography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Tomography
                Computed Axial Tomography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Ultrasound Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Ultrasound Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Ultrasound Imaging
                Computer and Information Sciences
                Software Engineering
                Software Tools
                Engineering and Technology
                Software Engineering
                Software Tools
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Pulmonary Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Pulmonary Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Pulmonary Imaging
                Research and Analysis Methods
                Imaging Techniques
                Custom metadata
                The datasets used in this publication ("MECANIX" and "ARTIFIX" from the Osirix Website: http://www.osirix-viewer.com/resources/dicom-image-library/) were freely available at the time of writing. Osirix has since changed access rights and requires a paid membership. As such, we are not authorised to distribute the datasets used for this study. However, the data is still publicly available but not free of charge.

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