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3-matic manualLog In or Register for exclusive website content. However, there are still without the precise step-by-step methods to design 3D navigation templates from computed tomography (CT) images. Our present article provides a detailed protocol to allow the readers or researchers to obtain the 3D navigation template easily, and assist with pedicle screw insertion in their future research and surgery. Using 3D navigation template-assisted pedicle screw fixation in spine surgery is low cost and can decrease the radiation exposure to both patients and surgeons. Accepted for publication Aug 10, 2018. Some surgeons use free hand to choose the entry point and trajectory of the pedicle screw. However, complications due to screw misplacement, such as nerve root injury, dural sac injury, and spine cord injury, are still reported, especial in cervical and upper thoracic spine ( 4, 5 ). In recent years, the development of 3D printing technique allows us to design the navigation templates to assist with accurate insertion of pedicle screws ( 8, 9 ). The design of navigation template is dependent on the surface that is extracted from the posterior features of the osseous spine. In this study, we provide a detailed protocol to allow the readers or researchers to design 3D navigation template, which help them to obtain the accurate insertion of pedicle screws. The data from thin-layer CT scans of the target spine is saved in format of DICOM. It is necessary to install Mimics and 3-Matic software on your computer; Firstly, open the software of Mimics. Click the left upper corner button: “File”, and choose the “New project wizard”, then, select the DICOM data which you want to use for 3D reconstruction, click the “Next”, the data will be imported into the software, click “Convert” at this time, the DICOM data software will be converted to the Figure 1; The target “New Mask” will be marked in green ( Figure 3 ). Then, click the “Calculate 3D” button.http://www.travira.by/img/3gjuice-manual.xml

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The “Calculate 3D” dialog box displayed at this time. We can select the “Green” Mask and “Optimal” (one of the quality levels). And click on the “Calculate”, the software will calculate the 3D image automatically now ( Figure 3 ), and after the 3D image reconstructed, the file can be saved in your local computer. T, B, A, P, L and R represent top, bottom, anterior, posterior, left, and right, respectively. If a previous reconstructed 3D spine is chosen, firstly, open the Mimics software, then, click “File” (left upper corner), choose the “Open project” and find your previous reconstructed 3D spine, click “Open” to open it. And we choose “Create Cylinder” in the “Medcad” module ( Figure 4 ), and move the cursor to a point of 3D object and click the left button of the mouse, this point is one of the ends of screw trajectory, then move the cursor to another point of 3D object and click the left button of the mouse, this point is the other the end of screw trajectory, the distance between above two points is the length of screw trajectory, to determine the radius, move the cursor to the side of the second point, and click the left button, the whole cylinder (screw trajectory) will be generated. Do it at other side again, and two screw trajectories will be created. You can drag one end of the trajectory adjust the screw trajectory; The dialog box will display ( Figure 5 ), you can click “unite”, meanwhile, click cylinder in left interface and 3D object in right interface—at this time the united object of screw trajectories and vertebrae is created. You can export the “STL” format of the united object on your local computer. Click the upper left corner button: File, import part, select your previous saved STL object (your object saved route), Next, Ok—the software will convert the STL data to Figure 6; We click “Mark” module ( Figure 6 ), select the “wave brush mark” to draw posterior surface to create the navigation template.http://ivankotov.ru/img/lib/3gjuice-manual.xmlThen, you can use it to print a 3D printing navigation template to guide the screw trajectory intra-operatively. However, the complication of pedicle screw misplacement is concerning with the free-hand approach, especially in thoracic and cervical spine region.The computer-assisted navigation system also has disadvantages of complex operation and inability to obtain the 3D image data and real-time computer reconstruction data ( 14, 15 ). They were fabricated by the milling machine and made of polycarbonate materials ( 18, 19 ). Then, Lu et al. used Mimics and UG Imageware to rapid prototyping drill template for lumbar and cervical pedicle screw fixation ( 16, 20 ). The designed templates accurately matched the posterior surface of lumbar vertebrae, and as a result, high accuracy of screw trajectory was achieved. This research was successfully performed on the thoracic vertebrae of cadavers ( 21 ) and scoliosis ( 22 ). All of the above studies showed navigation template provides the accuracy of screws placement. Here, we provide a protocol which will allow readers easily and reliably to design the 3D navigation template. The software of Mimics and 3-Matic software were used to design a navigation template in present article. The procedure of 3D reconstruction of the target segmental spine and pedicle screw trajectory simulation is similar to the Chen et al. ( 10 ), and we use the forth cervical vertebrae in present article. The Mimics and 3-Matic is in one software suite and worldwide used amongst researchers, because of it can help them convenient obtain the 3D reconstructions and surgically-oriented design. It is not only used in pedicle screw fixation, this technique may also be used in cortical bone trajectory fixation ( 23, 24 ), sacral screw fixation, sacral iliac screw fixation ( 25 ) and other special fixation ( 26, 27 ) approaches too. There are also other alternative methods to achieve navigation templates.http://www.drupalitalia.org/node/66049 The reasons of us to provide this protocol are because of its simplicity, convenience, reliability and convenience in designing the 3D navigation template. Y20170389), and Wenzhou Leading Talent Innovative Project (No. RX2016004). Pedicle screw instrumentation in adolescent idiopathic scoliosis (AIS).Pedicle screws enhance primary stability in multilevel cervical corpectomies: biomechanical in vitro comparison of different implants including constrained and nonconstrained posterior instumentations.A new design of internal fixation for scoliosis and its preliminary clinical application.Screw-related complications in the subaxial cervical spine with the use of lateral mass versus cervical pedicle screws: a systematic review.Risk of vertebral artery injury: comparison between C1-C2 transarticular and C2 pedicle screws.Radiation exposure to the spine surgeon during fluoroscopically assisted pedicle screw insertion.Radiation exposure during pedicle screw placement in adolescent idiopathic scoliosis: is fluoroscopy safe.Systematic review of 3D printing in spinal surgery: the current state of play.The accuracy of a method for printing three-dimensional spinal models.Three-dimensional reconstructions in spine and screw trajectory simulation on 3D digital images: a step by step approach by using Mimics software.Transpedicle screw fixation of the cervical spine.Accuracy of minimally invasive percutaneous thoracolumbar pedicle screws using 2D fluoroscopy: a retrospective review through 3D CT analysis.Comparative results between conventional and computer-assisted pedicle screw installation in the thoracic, lumbar, and sacral spine.Feasibility of CT-based intraoperative 3D stereotactic image-guided navigation in the upper cervical spine of children 10 years of age or younger: initial experience.Efficacy and accuracy of a novel rapid prototyping drill template for cervical pedicle screw placement.ASKUEANDCO.COM/images/3-m-manual.pdfComputer-assisted orthopedic surgery with individual templates and comparison to conventional operation method.Computer assisted orthopaedic surgery with image based individual templates.A novel computer-assisted drill guide template for lumbar pedicle screw placement: a cadaveric and clinical study.A novel computer-assisted drill guide template for thoracic pedicle screw placement: a cadaveric study.Accuracy and efficacy of thoracic pedicle screws in scoliosis with patient-specific drill template.The Feasibility of cortical bone trajectory screw fixation for lower thoracic spine.Systematic review of cortical bone trajectory versus pedicle screw techniques for lumbosacral spine fusion.The feasibility and radiological features of sacral alar iliac fixation in an adult population: a 3D imaging study.A radiological and cadaveric study of oblique lumbar interbody fixation in patients with normal spinal anatomy.Improving the trajectory of transpedicular transdiscal lumbar screw fixation with a computer-assisted 3D-printed custom drill guide.Design of a 3D navigation template to guide the screw trajectory in spine: a step-by-step approach using Mimics and 3-Matic software. All rights reserved.To benefit from the possibilities that 3D Printing offers, you need a versatile tool to make design modifications on the mesh level. In addition, it allows you to enhance your design by creating 3D textures, lightweight models and conformal structures, ready for Additive Manufacturing. Save Time and Costs with Lightweight Structures Materialise 3-matic allows you to create stunning lightweight designs, leading to lower material costs and less printing time. These optimized lattices outperform solid blocks of material in numerous ways. Master Part Properties Master the physical properties of your part. Change aerodynamic, acoustic and cushioning properties or characteristics, increase grip or control the density of your part with porous structures. Rely on a Complete Platform Materialise 3-matic is integrated with Materialise Magics and other solutions from our extensive software suite. Why choose Materialise 3-matic. Features of Materialise 3-matic Design Optimization Materialise 3-matic is a 3D Modeling software program that enables design optimization and modification on mesh level, using CAD designs, scanned data and topology optimized models as a starting point. Post Topology Optimization Materialise 3-matic lets you redesign rough surfaces outputted by topology optimization.The conversion will recognize the analytical shapes in your part and generates a fully parametric file without having to design from scratch. Modules Texturing Module Lightweight Structure CAD Import Module CAD Link Module Remesh Module Do you want to apply functional textures to your model or reduce the weight of your part with a lightweight structure. Choose the module that fits your needs. Use the Texturing Module to: It largely defines the esthetics of a part, but can also lead to added functionality or improved performance. In addition, you have the flexibility to quickly change and preview 3D textured components without having to share your designs with external suppliers. Lightweight Structures Module Manufacturing similar lightweight structures has always been challenging and often impossible to achieve with traditional manufacturing technologies. The Materialise 3-matic Lightweight Structures Module realizes one of the most promising potentials of the 3D printing industry: optimizing your designs and translating them into lightweight components. Key Benefits of the Lightweight Structures Module Materialise 3-matic’s interactive CAD import reads the surface structure of your CAD file and lets you create detailed surfaces by taking low-accuracy surfaces and reloading them with a higher resolution. As a combination of the segmentation tools and intelligent automatic splitting, this module offers you the complete solution to convert STL files to IGES or STEP. Remesh Module The surface mesh is optimized through different manual and automatic remesh operations, which control the quality and size of the part’s surface triangulation. These surface meshes and volume meshes can be exported to Fluent, Ansys, Abaqus, Comsol, Patran and Nastran file formats. Additionally, the lightweight beams designed in Materialise 3-matic can be exported to Abaqus and Optistruct, which allows you to fully analyze the structural integrity of your lightweight part before printing. Post-Topology Optimization A Quick Route to a Clean File Results from these packages are typically organic-looking STL files, but with very rough surface quality. Materialise 3-matic offers STL design tools that clean up the rough results of topology optimization, avoiding the elaborate step of re-building these organic files in CAD. All operations take place at STL level to exclude error-prone conversions to other file formats. Certain regions of the STL file can also be split to easily rebuild them as perfect CAD entities, such as cylinders, cones and planes. The smoothing and surface reconstruction operations are even able to redesign organic surfaces to minimize the amount of NURBS required to rebuild a potential CAD file. The Materialise 3-matic Remesh Module can also be used to clean up the mesh for an even faster FEA simulation. Using this module, organic surfaces can be exported with a minimal amount of CAD patches. Please try again.Download one of the Free Kindle apps to start reading Kindle books on your smartphone, tablet, and computer. Get your Kindle here, or download a FREE Kindle Reading App.If you are a seller for this product, would you like to suggest updates through seller support ? Amazon calculates a product’s star ratings using a machine learned model instead of a raw data average. The machine learned model takes into account factors including: the age of a review, helpfulness votes by customers and whether the reviews are from verified purchases. MATH-MATIC allowed for larger programs, automatically generating code to read overlay segments from UNISERVO tape as required.Philadelphia: Remington Rand Univac. Archived from the original (PDF) on 2014-12-26. Retrieved 2016-03-19. The Early Development of Programming Languages (Technical report). Computer Science Department, School of Humanities and Sciences, Stanford University. Retrieved 2016-03-19. Programming Languages: History and Fundamentals.Remington Rand Univac. 1958. Retrieved 2016-03-19. Online Historical Encyclopaedia of Programming Languages. Archived from the original on 2016-04-02. Retrieved 2016-03-20. Archived from the original on 2016-04-03. Retrieved 2016-03-20. By using this site, you agree to the Terms of Use and Privacy Policy. Unfortunately, WSD suffers from the well-known knowledge acquisition bottleneck problem: it is very expensive, in terms of both time and money, to acquire semantic annotations for a large number of sentences. To address this blocking issue we present Train-O-Matic, a knowledge-based and language-independent approach that is able to provide millions of training instances annotated automatically with word meanings. The approach is fully automatic, i.e., no human intervention is required, and the only type of human knowledge used is a task-independent WordNet-like resource. Moreover, as the sense distribution in the training set is pivotal to boosting the performance of WSD systems, we also present two unsupervised and language-independent methods that automatically induce a sense distribution when given a simple corpus of sentences. We show that, when the learned distributions are taken into account for generating the training sets, the performance of supervised methods is further enhanced. Experiments have proven that Train-O-Matic on its own, and also coupled with word sense distribution learning methods, lead a supervised system to achieve state-of-the-art performance consistently across gold standard datasets and languages. Importantly, we show how our sense distribution learning techniques aid Train-O-Matic to scale well over domains, without any extra human effort. To encourage future research, we release all the training sets in 5 different languages and the sense distributions for each domain of SemEval-13 and SemEval-15 at. Published by Elsevier B.V. Recommended articles No articles found. Citing articles Article Metrics View article metrics About ScienceDirect Remote access Shopping cart Advertise Contact and support Terms and conditions Privacy policy We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies. M. Butkus, NJ.Most other places It'll make you feel better, won't it. If you use Pay Pal, use the link below. Use the above address for a. Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. Our authors and editors We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too. Content Alerts Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective How it works Manage preferences Contact Want to get in touch. Contact our London head office or media team here Careers Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing. Although virtual presurgical planning offers more precise results, it may not be applied in every hospital because of the high costs. The aim of this study is to assess the accuracy of the suggested low-cost and effective surgical planning method by means of additive manufacturing to increase success rate of each surgery. In this study, a full spine model of a scoliosis patient was acquired and reconstructed in MIMICS software using different filters and parameters. Therefore, a comparison in terms of geometrical errors among each model was performed based on a reference model. Subsequently, patient-specific full spine model was manufactured using a three-dimensional printing method (fused deposition modeling) and utilized before the surgery. 3D surgical model reconstruction parameters such as wrap tool, binomial blur, and curvature flow filters produced high geometrical errors, while mean filter produced the lowest geometrical error. Furthermore, similarity results of the curvature flow and discrete Gaussian filters were close to mean filter. Smooth tool and mean filter produced almost the same volume of the reference model. Consequently, an ideal protocol for surgical planning of a spine surgery is defined with measurable accuracy. Thus, success rate of a spine surgery may be increased especially for the severe cases owing to the more accurate preoperative review: operability. The severity of scoliosis is also determined by Cobb method that provides obtain information about the curvature of the spine in terms of degrees. It is important to predict risk of progression, diagnose at early stage for preventing degenerative effects, and contribute to the patient’s quality of life. All operations require precise processes in a limited workspace because of the spinal nerves and blood vessels. Therefore, novel technologies and methods are of great importance to assist or plan the surgery in advance. Additive manufacturing, layer-by-layer fabrication of a physical object, is increasingly being used especially in the fields of medicine. Typical 2D digital imaging and communications in medicine (DICOM) files from the CT or MRI images are transformed into a 3D standard tessellation language (STL) file to perform 3D printing of the target model. In data acquisition step, a medical imaging system such as CT is used to obtain 2D DICOM images in general. High CNR values provide detailed segmentation before the 3D reconstruction of a model. These images are then processed via a commercial (Materialize Mimics) or an open-source (InVesalius) medical image processing software using the default segmentation functions or manual selection of the region of interest on each image during the segmentation step. Thus, the target anatomical model’s contour is masked on each image section. In 3D model reconstruction step, the mask images are positioned sequentially to form a solid model in 3D workspace of the software. Tissue-specific filters or data sets may be applied to minimize noise using predefined thresholds before and after the 3D reconstruction process. The solid model is exported as a STL file and prepared to be 3D printed. In 3D printing step, the STL file is 3D printed using one of the additive manufacturing methods such as fused deposition modeling (FDM), selective laser sintering (SLS), or stereolithography (SLA). Since surgical planning is important and plays a crucial role during the surgery, reference 3D model requires to be created with minimal geometrical errors. In this study, a spine model of a scoliosis patient is first acquired and then 3D reconstructed in MIMICS software (manual and automatic segmentation) using different filters and parameters to compare each geometrical error. The results of the DSC and HD values of each reconstructed model reveal the ideal protocol for surgical planning of a spine surgery and determine the accuracy of the created model. 2. Material and methods A 6-year-old patient (male) who has a congenital scoliosis history participated to this study for obtaining raw image data. Medical scanning process was performed at the Radiology Department, Faculty of Medicine Kocaeli University, using a CT scanner (Aquillion 64, Toshiba Medical Systems, Tokyo, Japan). A full spinal CT scanning was performed at 2 mm of slice thickness, 140 mm field of view (FoV) and 135 kV (40 mA, 1 s). Raw DICOM images (449 slices) were obtained from the picture archiving and communication system (PACS) server of the Radiology Department ( Figure 1(a) ). MIMICS software (v19) was utilized in image segmentation step, and a reference model was automatically reconstructed via MIMICS without any filters ( Figure 1(b) ). DICOM files (449 images) were imported, and segmentation process was performed using the thresholding tool at predefined threshold set of the bone (226, Hounsfield Unit, HU as lower threshold, and 3071 HU as higher threshold). These parameters were ideal to obtain exact contour of the target model in a maximum allowable noisy form according to the raw DICOM images for this case. Segmentation step was completed after each image section was masked and highlighted by a different color ( Figure 1(c) ). 3D calculation function was utilized at high quality to reconstruct the 3D raw surface, and a quite noisy model was created with desired contour based on the masked images in 3D model reconstruction step ( Figure 1(d) ). Each image was positioned sequentially to form a 3D raw model of the spine. Since there was noise in the workspace and model’s surface, the 3D raw model was directly exported to 3-matic software (v11, Materialize) for manual surface reconstruction and noise elimination ( Figure 1(e) ). The surface geometry of the model was manually revised, and the noise was manually cleared via 3-matic software according to the raw image geometries in MIMICS. Polygon area mark tool under the Mark—Area Mark menu was used to select each noisy surface and subsequently deleted. The gaps of the deleted surfaces were marked as bad contours on the 3D raw model. The bad contours were then selected and fixed, respectively, using the Fill Hole Freeform tool under the Fix menu. The Fill Hole Freeform process was performed at medium triangulation quality and created in tangent form. After manual surface reconstruction, the Fix Wizard tool under the Fix menu was utilized to fix geometrical normals, stitching, noise shells, holes, triangles, overlaps, and shells on the model. The errors were automatically fixed by clicking Follow Advice, Apply, and Update buttons, respectively, for each option ( Figure 1(f) ). Finally, the patient ID was added using the Quick Label tool under the Finish menu. Thus, 3D-reconstructed reference surgical model was obtained and exported as STL file by clicking File, Export, and STL buttons, respectively. The reference model was exported at binary format and one scaling factor. Some sharp cornered bonded surfaces that occurred by Fix Wizard tool during the fixing process were also revised in Meshmixer (v3.4.35, Autodesk) manually to obtain exact geometry of target anatomical model. The 3D model reconstruction step was completed after the reference model has taken its final shape (48 hours of work). 3D reconstruction of the reference surgical model is illustrated step by step in Figure 1. Test models were separated into three groups, namely, (i) preprocessed, (ii) post-processed, and (iii) fully processed models. Preprocessed models were obtained using the filtered DICOM images before the segmentation step without any post-process in MIMICS. Post-processed models were created from the raw DICOM images and only processed via 3-matic after the model was reconstructed ( Figure 2(a) ). Fully processed models were formed with a combination of both processes. Two different 3D tools of 3-matic, (i) Smooth ( Figure 2(g) ) and (ii) wrap ( Figure 2(h) ), were utilized separately for each sample in post-processing case. Pre-processing parameters of applied filters in MIMICS Model no. Before the calculation of HD and DSC values by CloudCompare software, each spine model was processed via 3-matic software that was necessary to determine the spine regions equally on each sample model and obtain more correct results in accuracy assessment. Therefore, the spine sections of each model were extracted manually and exported using the default options. The best results of the preprocessing and the post-processing cases were combined and applied together to form the fully processed model. Finally, the fully processed model was compared to the reference model to reveal the ideal solution for 3D surgical model reconstruction. Each generated test model is illustrated in Figure 2. HD of the test models was calculated by means of importing and analyzing the 3D models, the reference model and one of the test models at the same time, in CloudCompare workspace. Both models were then aligned using Registration Match bounding function—under the Tools menu. This process is required to align the box centers (volume frames) of models before performing any similarity function in order to obtain accurate results. Fine registration function under the Tools—Registration menu was applied on each model to highlight the difference of point clouds without any scale adjustment. The fine registration function also applies a rotation to compared model (generates a rotation matrix with a theoretical overlap value) to provide an ideal Overlap in volumes of the both reference and compared test model. DSC values were calculated similarly via importing the reference model and a test model into the 3D workspace of CloudCompare software. The match bounding and fine registration steps were also performed to obtain perfect alignment. After the alignment process, a plugin named as Cork, under the Plugins menu, was utilized to obtain the intersection of both models in terms of volume (cube units). Each volume was also measured by means of the mesh measure volume function under the Edit menu. Furthermore, HD and DSC results of the curvature flow and discrete Gaussian filters are close to mean filter. Smooth tool and mean filter produce almost the same volume of the reference model. However, binomial blur filter and wrap tool generate unacceptably different volumes. Moreover, the DSC results of the both mentioned functions are not overlapping properly. Each result is illustrated in Table 2. HD and DSC results of each test model No. Smooth and wrap tools generate undesired mesh structures caused by the noise during the segmentation step in MIMICS. HD results of each test model are illustrated in Figure 3. Figure 3.