Blog
About
Search
Advanced search

21
views
14
references
 Top references
27
cited by
 Most cited
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      699
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      From medical imaging data to 3D printed anatomical models

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 14

          • Record: found
          • Abstract: found
          • Article: not found

          Adaptive segmentation of MRI data.

           W.M. Wells,  W.E.L. Grimson,  R Kikinis … (1996)
          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.
          Article 0 comments Cited 112 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The application of rapid prototyping techniques in cranial reconstruction and preoperative planning in neurosurgery.

             Eberhard Uhl,  Paul Muller,  K. Gokula Krishnan … (2003)
            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.
            Article 0 comments Cited 34 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Simulation training in central venous catheter insertion: improved performance in clinical practice.

               Marina V. Evans,  Kayla Kaplan,  Esha Shah … (2010)
              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.
              Article 0 comments Cited 28 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Han-Chiao Isaac Chen: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 31 May 2017
                Publication date Collection: 2017
                Volume: 12
                Issue: 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
                Publisher ID: PONE-D-16-42523
                DOI: 10.1371/journal.pone.0178540
                PMC ID: 5451060
                PubMed ID: 28562693
                Copyright statement: © 2017 Bücking et al
                License:

                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
                Subject: Research Article
                Subject: Engineering and technology
                Subject: Electronics engineering
                Subject: 3D printing
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Musculoskeletal System
                Subject: Skeleton
                Subject: Ribs
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Musculoskeletal System
                Subject: Skeleton
                Subject: Ribs
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Ultrasound Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Ultrasound Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Ultrasound Imaging
                Subject: Computer and Information Sciences
                Subject: Software Engineering
                Subject: Software Tools
                Subject: Engineering and Technology
                Subject: Software Engineering
                Subject: Software Tools
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Pulmonary Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Pulmonary Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Pulmonary Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Custom metadata
                Data Availability 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.

                Data availability:

                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                Similar content 699

                See all similar

                Cited by 27

                See all cited by

                Most referenced authors 267

                See all reference authors