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Journal articles on the topic '3D Slicer'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Cahyati, Sally, and Haris Risqy Aziz. "The Influence of Different Slicer Software on 3d Printing Products Accuracy and Surface Roughness." Jurnal Rekayasa Mesin 12, no. 2 (August 15, 2021): 371–80. http://dx.doi.org/10.21776/ub.jrm.2021.012.02.14.

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Rapid Prototyping (RP) is a manufacturing process that produces a 3D model CAD to be a real product rapidly by using additive manufacturing technology. In this case, the product will print layer by layer uses a 3D printer machine. The 3D printer requires slicer software to convert CAD data into data that a 3D printer machine can read. Research is done to analyze the effect of three kinds of slicer software on 3D printing objects on the accuracy and surface roughness of the product. The 3D model CAD is sliced using three different slicer software, namely Ideamaker, Repetier Host, and Cura. The slice model result from each slicer will be printed on a 3D printer machine with the same process parameters to be compared. Then the product's dimensional and surface roughness will be measured to determine the effect of each slicer on product quality. The best quality of the product reflected the most suitable slicer software for the 3D printing machine that used. The best results achieved by Cura slicer because it has resulted in small dimensional deviations (max 0,0308±0,0079) and stabile high surface roughness of the product (max 1,585+059).

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Chen, Minhong, Zhong Li, Jianping Ding, Xingqi Lu, Yinan Cheng, and Jiayun Lin. "Comparison of Common Methods for Precision Volume Measurement of Hematoma." Computational and Mathematical Methods in Medicine 2020 (July 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/6930836.

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Purpose. Our aim is to conduct analysis and comparison of some methods commonly used to measure the volume of hematoma, for example, slice method, voxelization method, and 3D-Slicer software method (projection method). Method. In order to validate the accuracy of the slice method, voxelization method, and 3D-Slicer method, these three methods were first applied to measure two known volumetric models, respectively. Then, a total of 198 patients diagnosed with spontaneous intracerebral hemorrhage (ICH) were recruited. The patients were split into 3 different groups based on the hematoma size: group 1: volume<10 ml (n=89), group 2: volume between 10 and 20 ml (n=59), and group 3: volume>20 ml (n=50). And the shape of the hematoma was classed into regular (round to ellipsoid) with smooth margins (n=76), irregular with frayed margins (n=85), and multilobular (n=37). The slice method, voxelization method, and 3D-Slicer method were adopted to measure the volume of hematoma, respectively, considering the nonclosed models and the models which may contain inaccurate normal information during CT scan. Moreover, the results were compared with the 3D-Slicer method for closed models. Results. There was a significant estimation error (P<0.05) using these three methods to calculate the volume of the closed hematoma model. The estimated hematoma volume was calculated to be 14.2086743±0.900559087 ml, 14.2119130±0.900851812 ml, and 14.2123825±0.900835916 ml using slice method 1, slice method 2, and the voxelization method, respectively, compared to 14.212656±0.900992371 ml using the 3D-Slicer method. The mean estimation error was -0.00398172 ml, -0.00074303 ml, and -0.00027354 ml caused by slice method 1, slice method 2, and voxelization method, respectively. There was a significant estimation error (P<0.05), applying these three methods to calculate the volume of the nonclosed hematoma model. The estimated hematoma volume was calculated to be 14.1928246±0.902210314 ml using the 3D-Slicer method. The mean estimation error was calculated to be -0.00402121 ml, -0.00078237 ml, -0.00031288 ml, and -0.01983136 ml using slice method 1, slice method 2, voxelization method, and 3D-Slicer method, respectively. Conclusions. The 3D-Slicer software method is considered as a stable and capable method of high precision for the calculation of a closed hematoma model with correct normal direction, while it would be inappropriate for the nonclosed model nor the model with incorrect normal direction. The slice method and voxelization method can be the supplement and improvement of the 3D-Slicer software method, for the purpose of achieving precision medicine.

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3

Bruns, Nico. "3D Slicer." Der Unfallchirurg 122, no. 8 (July 8, 2019): 662–63. http://dx.doi.org/10.1007/s00113-019-0654-4.

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4

Wang, Jian Jun, and Xue Jun Wang. "The 3D Modeling Design of Cutting Machine Based on Solidworks." Advanced Materials Research 945-949 (June 2014): 77–80. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.77.

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According to design requirements of the slicer,3D model was assembled in SolidWorks;Alternating motion of the slicer applies slider-crank mechanism.With the friction wheel cover,friction wheel can be arbitrary thickness within a certain range of sections. Using the SolidWorks software development can shorten design time and save money.

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Simmross-Wattenberg, Federico, Noemí Carranza-Herrezuelo, Cristina Palacios-Camarero, Pablo Casaseca-de-la-Higuera, Miguel Ángel Martín-Fernández, Santiago Aja-Fernández, Juan Ruiz-Alzola, Carl-Fredrik Westin, and Carlos Alberola-López. "Group-Slicer: A collaborative extension of 3D-Slicer." Journal of Biomedical Informatics 38, no. 6 (December 2005): 431–42. http://dx.doi.org/10.1016/j.jbi.2005.03.001.

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6

Pinter, Csaba, Andras Lasso, An Wang, David Jaffray, and Gabor Fichtinger. "SlicerRT: Radiation therapy research toolkit for 3D Slicer." Medical Physics 39, no. 10 (September 27, 2012): 6332–38. http://dx.doi.org/10.1118/1.4754659.

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7

Liao, Rongfang, Longmao Liu, Bo Song, Xinhong Wan, Shuo Wang, and Jianhong Xu. "3D-Slicer Software-Assisted Neuroendoscopic Surgery in the Treatment of Hypertensive Cerebral Hemorrhage." Computational and Mathematical Methods in Medicine 2022 (February 18, 2022): 1–7. http://dx.doi.org/10.1155/2022/7156598.

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Objective. To explore the 3D-slicer software-assisted endoscopic treatment for patients with hypertensive cerebral hemorrhage. Methods. A total of 120 patients with hypertensive cerebral hemorrhage were selected and randomly divided into control group and 3D-slicer group with 60 cases each. Patients in the control group underwent traditional imaging positioning craniotomy, and patients in the 3D-slicer group underwent 3D-slicer followed by precision puncture treatment. In this paper, we evaluate the hematoma clearance rate, nerve function, ability of daily living, complication rate, and prognosis. Results. The 3D-slicer group is better than the control group in various indicators. Compared with the control group, the 3D-slicer group has lower complications, slightly higher hematoma clearance rate, and better recovery of nerve function and daily living ability before and after surgery. The incidence of poor prognosis is low. Conclusion. The 3D-slicer software-assisted endoscopic treatment for patients with hypertensive intracerebral hemorrhage has a better hematoma clearance effect, which is beneficial to the patient’s early recovery and reduces the damage to the brain nerve of the patient.

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Zeineldin, Ramy A., Pauline Weimann, Mohamed E. Karar, Franziska Mathis-Ullrich, and Oliver Burgert. "Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation." Current Directions in Biomedical Engineering 7, no. 1 (August 1, 2021): 30–34. http://dx.doi.org/10.1515/cdbme-2021-1007.

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Abstract Purpose Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer- DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AIassisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.

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Qiu, Shi Yin, Hai Tao Wu, and Hong Bin Liu. "Design and Simulation of Pastry Slicer Based on SolidWorks and ADAMS." Advanced Materials Research 328-330 (September 2011): 151–54. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.151.

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A new type of pastry slicer which could alter pastry thickness expediently was designed by using SolidWorks and ADAMS. In the first place, the pastry slicer 3D model was established by SolidWorks and imported into ADAMS by the nicer interface between SolidWorks and ADAMS. In the second place, the pastry slicer 3D model was simulated in ADAMS. The dynamics simulation result showed that the pastry slicer could fulfil the anticipative design requirements. The simulation analysis results could be regard as the design reference for the new type of pastry slicer, and the virtual prototype could be used to do a deeper analysis according to different requirements.

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Wang, Hua-wei, Chen Wu, Zhe Xue, Xu-jun Shu, and Zheng-hui Sun. "A Supplemental Technique for Preoperative Evaluation of Giant Intracranial Aneurysm." Journal of Neurological Surgery Part A: Central European Neurosurgery 82, no. 05 (February 14, 2021): 424–29. http://dx.doi.org/10.1055/s-0040-1721006.

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Abstract Background Preoperative planning mainly relies on digital subtraction angiography (DSA) and computed tomography angiography. However, neither technique can reveal thrombi in giant intracranial aneurysms (GIAs). In this study, we aimed to reconstruct the circulating and noncirculating parts of GIAs with the time-of-flight (TOF) and motion-sensitized driven-equilibrium (MSDE) sequences with 3D Slicer to reveal an integrated presentation of GIAs, compare its accuracy, and validate the usefulness for preoperative planning. Material and Methods Patients with GIAs who were treated with microsurgery in our department were included in this study. Both the TOF and MSDE sequence data for each patient were loaded into 3D Slicer for reconstruction and segmentation. The parameters measured by 3D Slicer were compared with those measured by DSA. Results The mean diameter for all GIAs was 28.7 ± 1.5 mm (range, 25.9–31.9 mm). The mean diameter for all GIAs measured by DSA and 3D Slicer was 24.46 ± 5.25 and 28.66 ± 1.48 mm, respectively (t = 4.948, p < 0.01). When only the nonthrombotic GIAs were included, the mean diameter measured by DSA and 3D Slicer was 28.69 ± 2.03 and 28.97 ± 1.79 mm, respectively (t = 1.023, p = 0.323). The mean aneurysmal volume was 8,292.6 ± 1,175.1 mm3 and the mean thrombotic volume was 3,590.0 ± 1,003.7 mm3. Conclusion The MSDE sequence brings diagnostic benefits as a comparison to other MRI sequences. Reconstruction of GIAs with 3D Slicer is a low-cost, dependable, and useful supplemental technique for surgical planning.

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Egger, Jan, Tina Kapur, Christopher Nimsky, and Ron Kikinis. "Pituitary Adenoma Volumetry with 3D Slicer." PLoS ONE 7, no. 12 (December 11, 2012): e51788. http://dx.doi.org/10.1371/journal.pone.0051788.

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Cheng, George Z., Raul San Jose Estepar, Erik Folch, Jorge Onieva, Sidhu Gangadharan, and Adnan Majid. "Three-dimensional Printing and 3D Slicer." Chest 149, no. 5 (May 2016): 1136–42. http://dx.doi.org/10.1016/j.chest.2016.03.001.

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13

Zaffino, Paolo, Alessio Merola, Domenico Leuzzi, Virgilio Sabatino, Carlo Cosentino, and Maria Francesca Spadea. "SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller." Bioengineering 7, no. 3 (September 11, 2020): 109. http://dx.doi.org/10.3390/bioengineering7030109.

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Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure. Deep integration with 3D Slicer code environment is provided and a basic input–output mechanism accessible via GUI is also made available. To test the proposed extension, two exemplary use cases were implemented: (1) INPUT data to 3D Slicer, to navigate on basis of data detected by a distance sensor connected to the board, and (2) OUTPUT data from 3D Slicer, to control a servomotor on the basis of data computed through image process procedures. Both goals were achieved and quasi-real-time control was obtained without any lag or freeze, thus boosting the integration between 3D Slicer and Arduino. This integration can be easily obtained through the execution of few lines of Python code. In conclusion, SlicerArduino proved to be suitable for fast prototyping, basic input–output interaction, and educational purposes. The extension is not intended for mission-critical clinical tasks.

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Bryła, Jakub, and Adam Martowicz. "Study on the Importance of a Slicer Selection for the 3D Printing Process Parameters via the Investigation of G-Code Readings." Machines 9, no. 8 (August 11, 2021): 163. http://dx.doi.org/10.3390/machines9080163.

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The work deals with the investigation of the variation of the selected 3D printing process properties that originate from the choice of a slicer. Specifically, the main aim of the present study was to assess the induced changes of the kinematic and geometric properties considered by the slicer for the printing process making use of the G-code readings. The paper provides adequate definitions and formulas required to characterize the slicer’s configuration. Next, the selected cases of the process parameters’ changes were studied, primarily taking into account varying layer height and infill. The authors performed a detailed analysis regarding the geometric implications at the mesoscale due to the slicer’s settings. Appropriate modifications of the slicer’s properties were also proposed and verified, making it possible to match the geometric and kinematic characteristics of the printed part.

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McPherson, Jace, and Wenchao Zhou. "A chunk-based slicer for cooperative 3D printing." Rapid Prototyping Journal 24, no. 9 (November 12, 2018): 1436–46. http://dx.doi.org/10.1108/rpj-07-2017-0150.

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Purpose The purpose of this research is to develop a new slicing scheme for the emerging cooperative three-dimensional (3D) printing platform that has multiple mobile 3D printers working together on one print job. Design/methodology/approach Because the traditional lay-based slicing scheme does not work for cooperative 3D printing, a chunk-based slicing scheme is proposed to split the print job into chunks so that different mobile printers can print different chunks simultaneously without interfering with each other. Findings A chunk-based slicer is developed for two mobile 3D printers to work together cooperatively. A simulator environment is developed to validate the developed slicer, which shows the chunk-based slicer working effectively, and demonstrates the promise of cooperative 3D printing. Research limitations/implications For simplicity, this research only considered the case of two mobile 3D printers working together. Future research is needed for a slicing and scheduling scheme that can work with thousands of mobile 3D printers. Practical implications The research findings in this work demonstrate a new approach to 3D printing. By enabling multiple mobile 3D printers working together, the printing speed can be significantly increased and the printing capability (for multiple materials and multiple components) can be greatly enhanced. Social implications The chunk-based slicing algorithm is critical to the success of cooperative 3D printing, which may enable an autonomous factory equipped with a swarm of autonomous mobile 3D printers and mobile robots for autonomous manufacturing and assembly. Originality/value This work presents a new approach to 3D printing. Instead of printing layer by layer, each mobile 3D printer will print one chunk at a time, which provides the much-needed scalability for 3D printing to print large-sized object and increase the printing speed. The chunk-based approach keeps the 3D printing local and avoids the large temperature gradient and associated internal stress as the size of the print increases.

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Song, Yanzhi, Zhouwang Yang, Yuan Liu, and Jiansong Deng. "Function representation based slicer for 3D printing." Computer Aided Geometric Design 62 (May 2018): 276–93. http://dx.doi.org/10.1016/j.cagd.2018.03.012.

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Nayak, Ramyasri, Nandish S, and Prakashini Koteshwara. "Volume Estimation of Pulmonary Lesion Using Chest CT Sequence." International Journal of Engineering & Technology 7, no. 3.1 (August 4, 2018): 186. http://dx.doi.org/10.14419/ijet.v7i3.1.17234.

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Many of the imaging modalities like X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), fMRI have emerged to capture high quality images of anatomical structures of the human body. Radiologist can also have a better visualization if the regions of interest in the images are extracted and visualized 3D. To extract region of interest, sometimes preprocessing steps are performed on the input data. Pulmonary lesion is a small round or oval-shaped growth in the lung. It consists of solid and non-solid portion. The estimation of solid and non-solid portion of the pulmonary nodules will help the clinicians in the diagnosis and to suggest the appropriate treatment methodology. Lesion volume estimation gives a brief idea about the area occupied by the lesion tissues, which in turn can help the radiologist to plan treatment accordingly. In proposed work, lesion is segmented using K-means algorithm and then volume of the lesion is estimated. The slices which have segmented lesion with solid and non-solid regions is used for 3D visualization. The results obtained using the proposed methodology is validated with the Slicer 3D software. Error in the estimated volume of the solid and non-solid portion of the lesion was found to be in the range of 1.11% - 3.30% and 0.1% to 4.55% respectively. Results from the proposed methodology, lesion extraction with solid and non-solid, 3D visualization of the same and volume estimation respectively are validated by taking feedback from the radiologists and segmented lesion slices are used to estimate the volume and 3D visualization in Slicer 3D software for validation.

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GOMEZ ALONSO, JOSE LUIS, ALEXANDRA ALLUE SALVADOR, ISABEL DE MARCO RODRIGUEZ, JONE RETOLAZA GAVIÑA, and GORKA DIEZ BARCENILLA. "INFLUENCE OF SLICER SOFTWARE USED WITH 3D PRINTING FILAMENT EXTRUSION TECHNOLOGY ON PROPERTIES OF PRINTED PARTS WITH SHORT FIBER REINFORCED THERMOPLASTIC COMPOSITE." DYNA 97, no. 3 (May 1, 2022): 295–300. http://dx.doi.org/10.6036/10285.

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3D printing with filament extrusion technology is a process in which the thermoplastic molten material is sequentially accumulated layer by layer on a construction platform. 3D printers use a slicer software that converts 3D digital models into printing instructions. The software calculates the trajectory of the extruder within the printer, from three-dimensional mesh-based models. The input of the lamination software is a “.stl” file and the output is a “.gcode” file. For each layer, the lamination program generates the path that the printer extruder will travel, this could determine if a part is correctly made or not. The objective of this study has been to analyze, with a view to industry implementation of the 3D printing, if the slicer software used in the extrusion 3D printing of short glass fiber reinforced thermoplastic, influences the final results that are obtained in the printed parts. It has been worked with four 3D printing slicer software, and it has been analyzed if there are significantly differences in mechanical and dimensional properties in the printed parts. For which, in the printing tests, always the same filament printer, printing material, part design, ".stl" file, and values for the percentage and type of filling, height layer and skin thickness have been used. The additive material used was a 2.85 mm diameter filament of 30% short glass fiber reinforced polyamide 6. It has been concluded that the slicer software, considering the evaluated ones, influences the results of the percentage of deformation at maximum tension, of the printed parts. The influence on the values of the resistance and the tensile modulus is not significant. Keywords: Filament fused fabrication (FFF), fused deposition modeling (FDM), short fibre composites, slicer software, printing parameters, tensile strength.

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Zhang, Tianyu, Guoxin Fang, Yuming Huang, Neelotpal Dutta, Sylvain Lefebvre, Zekai Murat Kilic, and Charlie C. L. Wang. "S ³ -Slicer." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–15. http://dx.doi.org/10.1145/3550454.3555516.

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Multi-axis motion introduces more degrees of freedom into the process of 3D printing to enable different objectives of fabrication by accumulating materials layers upon curved layers. An existing challenge is how to effectively generate the curved layers satisfying multiple objectives simultaneously. This paper presents a general slicing framework for achieving multiple fabrication objectives including support free, strength reinforcement and surface quality. These objectives are formulated as local printing directions varied in the volume of a solid, which are achieved by computing the rotation-driven deformation for the input model. The height field of a deformed model is mapped into a scalar field on its original shape, the isosurfaces of which give the curved layers of multi-axis 3D printing. The deformation can be effectively optimized with the help of quaternion fields to achieve the fabrication objectives. The effectiveness of our method has been verified on a variety of models.

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Connolly, Laura, Anton Deguet, Simon Leonard, Junichi Tokuda, Tamas Ungi, Axel Krieger, Peter Kazanzides, Parvin Mousavi, Gabor Fichtinger, and Russell H. Taylor. "Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions." Sensors 22, no. 14 (July 17, 2022): 5336. http://dx.doi.org/10.3390/s22145336.

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Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarly, for robotics, the open-source middleware suite robot operating system (ROS) is the standard development framework. In the past, there have been several “ad hoc” attempts made to bridge both tools; however, they are all reliant on middleware and custom interfaces. Additionally, none of these attempts have been successful in bridging access to the full suite of tools provided by ROS or 3D Slicer. Therefore, in this paper, we present the SlicerROS2 module, which was designed for the direct use of ROS2 packages and libraries within 3D Slicer. The module was developed to enable real-time visualization of robots, accommodate different robot configurations, and facilitate data transfer in both directions (between ROS and Slicer). We demonstrate the system on multiple robots with different configurations, evaluate the system performance and discuss an image-guided robotic intervention that can be prototyped with this module. This module can serve as a starting point for clinical system development that reduces the need for custom interfaces and time-intensive platform setup.

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Alexander, K. M., A. Robinson, C. Pinter, G. Fichtinger, and L. J. Schreiner. "Development of 3D Slicer based film dosimetry analysis." Journal of Physics: Conference Series 847 (May 2017): 012061. http://dx.doi.org/10.1088/1742-6596/847/1/012061.

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Chang, Jincai, Jianzhong Cui, Yuxuan Wang, Han Wang, and Keqin Shen. "Brain CT image segmentation based on 3D slicer." Journal of Complexity in Health Sciences 3, no. 1 (June 30, 2020): 34–42. http://dx.doi.org/10.21595/chs.2020.21263.

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Chang, Jincai, Jianzhong Cui, Liyuan Ma, Chaoqi Zhang, and Weina Wu. "Secondary development based on 3D Slicer extension modules." Journal of Complexity in Health Sciences 3, no. 1 (June 30, 2020): 73–80. http://dx.doi.org/10.21595/chs.2020.21267.

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Mohd Ariffin, M. K. A., N. A. Sukindar, B. T. H. T. Baharudin, C. N. A. Jaafar, and M. I. S. Ismail. "Slicer Method Comparison Using Open-source 3D Printer." IOP Conference Series: Earth and Environmental Science 114 (January 2018): 012018. http://dx.doi.org/10.1088/1755-1315/114/1/012018.

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Chalupa, Daniel, and Jan Mikulka. "A Novel Tool for Supervised Segmentation Using 3D Slicer." Symmetry 10, no. 11 (November 12, 2018): 627. http://dx.doi.org/10.3390/sym10110627.

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The rather impressive extension library of medical image-processing platform 3D Slicer lacks a wide range of machine-learning toolboxes. The authors have developed such a toolbox that incorporates commonly used machine-learning libraries. The extension uses a simple graphical user interface that allows the user to preprocess data, train a classifier, and use that classifier in common medical image-classification tasks, such as tumor staging or various anatomical segmentations without a deeper knowledge of the inner workings of the classifiers. A series of experiments were carried out to showcase the capabilities of the extension and quantify the symmetry between the physical characteristics of pathological tissues and the parameters of a classifying model. These experiments also include an analysis of the impact of training vector size and feature selection on the sensitivity and specificity of all included classifiers. The results indicate that training vector size can be minimized for all classifiers. Using the data from the Brain Tumor Segmentation Challenge, Random Forest appears to have the widest range of parameters that produce sufficiently accurate segmentations, while optimal Support Vector Machines’ training parameters are concentrated in a narrow feature space.

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HONG, Jaesung, Daisuke INOUE, Kouji YOSHIMOTO, Tomio SASAKI, and Makoto HASHIZUME. "0810 3D Slicer-based Neurosurgical Navigation Using Image Fusion." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2009.22 (2010): 134. http://dx.doi.org/10.1299/jsmebio.2009.22.134.

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Chang, Jincai, Jianzhong Cui, Shuhan Jin, Guoqing Yu, and Jingyu Song. "Development environment construction of medical imaging software 3d slicer." Journal of Complexity in Health Sciences 3, no. 1 (June 30, 2020): 43–51. http://dx.doi.org/10.21595/chs.2020.21264.

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Alexander, K. M., C. Pinter, G. Fichtinger, T. Olding, and L. J. Schreiner. "Streamlined open-source gel dosimetry analysis in 3D slicer." Biomedical Physics & Engineering Express 4, no. 4 (July 19, 2018): 045041. http://dx.doi.org/10.1088/2057-1976/aad0cf.

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Nardelli, Pietro, Alexander Jaeger, Conor O’Shea, Kashif A. Khan, Marcus P. Kennedy, and Pádraig Cantillon-Murphy. "Pre-clinical validation of virtual bronchoscopy using 3D Slicer." International Journal of Computer Assisted Radiology and Surgery 12, no. 1 (June 21, 2016): 25–38. http://dx.doi.org/10.1007/s11548-016-1447-7.

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Levine, Lance, and Marc Levine. "DRRGenerator: A Three-dimensional Slicer Extension for the Rapid and Easy Development of Digitally Reconstructed Radiographs." Journal of Clinical Imaging Science 10 (October 29, 2020): 69. http://dx.doi.org/10.25259/jcis_105_2020.

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As the interest in image-guided medical interventions has increased, so too has the necessity for open-source software tools to provide the required capabilities without exorbitant costs. A common issue encountered in these procedures is the need to compare computed tomography (CT) data with X-ray data, for example, to compare pre-operative CT imaging with intraoperative X-rays. A software approach to solve this dilemma is the production of digitally reconstructed radiographs (DRRs) which computationally simulate an X-ray-type image from CT data. The resultant image can be easily compared to an X-ray image and can provide valuable clinical information, such as small anatomical changes that have occurred between the pre-operative and operative imaging (i.e., vertebral positioning). To provide an easy way for clinicians to make their own DRRs, we propose DRR generator, a customizable extension for the open-source medical imaging application three-dimensional (3D) Slicer. DRR generator provides rapid computation of DRRs through a highly customizable user interface. This extension provides end-users a free, open-source, and reliable way of generating DRRs. This program is integrated within 3D Slicer and thus can utilize its powerful imaging tools to provide a comprehensive segmentation and registration application for clinicians and researchers. DRR generator is available for download through 3D Slicer’s in-app extension manager and requires no additional software.

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Deshazer, Garron, Derek Merck, Scott Collins, Yakub M. Puthawala, and Jose Garcia Cobian. "Visualization of the 3D Dosimetry for a Leipzig Brachytherapy Applicator Using 3D Slicer." Brachytherapy 15 (May 2016): S148. http://dx.doi.org/10.1016/j.brachy.2016.04.260.

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King, Franklin, Jagadeesan Jayender, Sharath K. Bhagavatula, Paul B. Shyn, Steve Pieper, Tina Kapur, Andras Lasso, and Gabor Fichtinger. "An Immersive Virtual Reality Environment for Diagnostic Imaging." Journal of Medical Robotics Research 01, no. 01 (March 2016): 1640003. http://dx.doi.org/10.1142/s2424905x16400031.

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Purpose: Advancements in and adoption of consumer virtual reality (VR) are currently being propelled by numerous upcoming devices such as the Oculus Rift. Although applications are currently growing around the entertainment field, wide-spread adoption of VR devices opens up the potential for other applications that may have been unfeasible with past implementations of VR. A VR environment may provide an equal or larger screen area than what is provided with the use of multiple conventional displays while remaining comparatively cheaper and more portable making it an attractive option for diagnostic radiology applications. Methods A VR application for the viewing of multiple image slices was designed using: the Oculus Rift head-mounted display (HMD), Unity, and 3D Slicer. Volumes loaded within 3D Slicer are sent to a Unity application that proceeds to render a scene for the Oculus Rift HMD. Users may interact with the images adjusting windowing and leveling using a handheld gamepad controller. Multiple images may be brought closer to the user for detailed inspection. Results Application usage was demonstrated with the simultaneous visualization of longitudinal slices of a serial CT scan of a patient with a lung nodule. Pilot studies for validating usage of the VR system for differential diagnosis and remote collaboration were performed. Initial results suggest that using the VR system increased both task load and time taken to complete tasks, however, the resulting accuracy in assessing nodule growth of nodules was not significantly different than that achieved using a DICOM viewer application on a traditional display.

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Mandolini, Marco, Agnese Brunzini, Giulia Facco, Alida Mazzoli, Archimede Forcellese, and Antonio Gigante. "Comparison of Three 3D Segmentation Software Tools for Hip Surgical Planning." Sensors 22, no. 14 (July 13, 2022): 5242. http://dx.doi.org/10.3390/s22145242.

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In hip arthroplasty, preoperative planning is fundamental to reaching a successful surgery. Nowadays, several software tools for computed tomography (CT) image processing are available. However, research studies comparing segmentation tools for hip surgery planning for patients affected by osteoarthritic diseases or osteoporotic fractures are still lacking. The present work compares three different software from the geometric, dimensional, and usability perspectives to identify the best three-dimensional (3D) modelling tool for the reconstruction of pathological femoral heads. Syngo.via Frontier (by Siemens Healthcare) is a medical image reading and post-processing software that allows low-skilled operators to produce prototypes. Materialise (by Mimics) is a commercial medical modelling software. 3D Slicer (by slicer.org) is an open-source development platform used in medical and biomedical fields. The 3D models reconstructed starting from the in vivo CT images of the pathological femoral head are compared with the geometries obtained from the laser scan of the in vitro bony specimens. The results show that Mimics and 3D Slicer are better for dimensional and geometric accuracy in the 3D reconstruction, while syngo.via Frontier is the easiest to use in the hospital setting.

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Baldi, D., M. Aiello, A. Duggento, M. Salvatore, and C. Cavaliere. "MR Imaging-Histology Correlation by Tailored 3D-Printed Slicer in Oncological Assessment." Contrast Media & Molecular Imaging 2019 (May 29, 2019): 1–9. http://dx.doi.org/10.1155/2019/1071453.

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3D printing and reverse engineering are innovative technologies that are revolutionizing scientific research in the health sciences and related clinical practice. Such technologies are able to improve the development of various custom-made medical devices while also lowering design and production costs. Recent advances allow the printing of particularly complex prototypes whose geometry is drawn from precise computer models designed on in vivo imaging data. This review summarizes a new method for histological sample processing (applicable to e.g., the brain, prostate, liver, and renal mass) which employs a personalized mold developed from diagnostic images through computer-aided design software and 3D printing. Through positioning the custom mold in a coherent manner with respect to the organ of interest (as delineated by in vivo imaging data), the cutting instrument can be precisely guided in order to obtain blocks of tissue which correspond with high accuracy to the slices imaged. This approach appeared crucial for validation of new quantitative imaging tools, for an accurate imaging-histopathological correlation and for the assessment of radiogenomic features extracted from oncological lesions. The aim of this review is to define and describe 3D printing technologies which are applicable to oncological assessment and slicer design, highlighting the radiological and pathological perspective as well as recent applications of this approach for the histological validation of and correlation with MR images.

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Chang, Jincai, Xiaolin Zhang, Kexin Zhang, and Qiuling Pan. "Three-dimensional reconstruction of medical images based on 3D slicer." Journal of Complexity in Health Sciences 2, no. 1 (June 30, 2019): 1–12. http://dx.doi.org/10.21595/chs.2019.20724.

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Isurugi, Chizuko, Rie Oyama, Gen Haba, Yuri Sasaki, Yoshitaka Kaido, Tomonobu Kanasugi, Akihiko Kikuchi, and Toru Sugiyama. "To visualize the architecture of placenta using 3D Slicer software." Placenta 34, no. 10 (October 2013): A12. http://dx.doi.org/10.1016/j.placenta.2013.07.046.

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Lansdown, Drew A., Robert Dawe, Gregory L. Cvetanovich, Nikhil N. Verma, Brian J. Cole, Bernard R. Bach, Gregory P. Nicholson, Anthony A. Romeo, and Adam Blair Yanke. "Automated 3D MRI Allows for Accurate Evaluation of Glenoid Bone Loss as Compared to 3D CT." Orthopaedic Journal of Sports Medicine 6, no. 7_suppl4 (July 1, 2018): 2325967118S0008. http://dx.doi.org/10.1177/2325967118s00089.

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Objectives: Glenoid bone loss is frequently present in the setting of recurrent shoulder instability. The magnitude of bone loss is an important determinant of the optimal surgical treatment. The current gold-standard for measurement of glenoid bone loss is three-dimensional (3D) reconstruction of a computed tomography (CT) scan. CT scans, however, carry an inherent risk of radiation and increased cost for a second modality. Magnetic resonance imaging (MRI) offers excellent soft tissue contrast and may allow resolution of bony structures to generate 3D reconstructions without a risk of ionizing radiation. We hypothesized that automated 3D MRI reconstruction would offer similar measurements of glenoid bone loss as recorded from a 3D CT scan in a clinical setting. Methods: A retrospective review was performed for fourteen patients who had both pre-operative MRI scan and CT scan of the shoulder. All MR scans were performed on a 1.5 T scanner (Siemens) utilizing a Dixon chemical shift separation sequence and the out-of-phase images with 0.90 mm slice thickness. Reconstructions of the glenoid were performed from axial images (Figure 1A) using an open-platform image processing system (3D Slicer; slicer.org). A single point on the glenoid was selected and a standard threshold was used to build a 3D model (Figure 1B). High-resolution CT scans underwent 3D reconstruction in Slicer based on Houndsfield Unit thresholding. Glenoid bone loss on both scans was measured with the Pico method by defining a circle of best fit using the inferior 2/3 of the glenoid and determining the percent area missing from this circle. Pearson’s correlation coefficient was utilized to determine the similarity between MR and CT based measurements. Statistical significance was defined as p<0.05. Results: The correlation between 3D MR and CT-based measurements of glenoid bone loss was excellent (r = 0.95, p<0.0001). The mean bone loss as measured by the 3D MR was 13.2 +- 7.2% and was 12.5 +- 8.6% for the 3D CT reconstruction (p=0.32). Bone loss in this cohort ranged from 3.7-25.4% on 3D MR and 1.4-26.0% on 3D CT. The root-mean-square difference between measurements was 2.7%. Conclusion: There was excellent agreement between automated 3D MR and 3D CT measurements of glenoid bone loss and minimal differences between these measurements. This reconstruction method requires minimal post-processing, no manual segmentation, and is obtained with widely-available MR sequences. This method has the potential to decrease the utilization for CT scans in determining glenoid bone loss. [Figure: see text]

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Lo Giudice, Antonino, Vincenzo Quinzi, Vincenzo Ronsivalle, Marco Farronato, Carmelo Nicotra, Francesco Indelicato, and Gaetano Isola. "Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandibular Condyle. A Comparative Study Using a Surface-to-Surface Matching Technique." International Journal of Environmental Research and Public Health 17, no. 13 (July 3, 2020): 4789. http://dx.doi.org/10.3390/ijerph17134789.

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The aim of this study was to assess the accuracy of 3D rendering of the mandibular condylar region obtained from different semi-automatic segmentation methodology. A total of 10 Cone beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the condyles by using three free-source software (Invesalius, version 3.0.0, Centro de Tecnologia da Informação Renato Archer, Campinas, SP, Brazil; ITK-Snap, version2.2.0; Slicer 3D, version 4.10.2) and one commercially available software Dolphin 3D (Dolphin Imaging, version 11.0, Chatsworth, CA, USA). The same models were also manually segmented (Mimics, version 17.01, Materialise, Leuven, Belgium) and set as ground truth. The accuracy of semi-automatic segmentation was evaluated by (1) comparing the volume of each semi-automatic 3D rendered condylar model with that obtained with manual segmentation, (2) deviation analysis of each 3D rendered mandibular models with those obtained from manual segmentation. No significant differences were found in the volumetric dimensions of the condylar models among the tested software (p > 0.05). However, the color-coded map showed underestimation of the condylar models obtained with ITK-Snap and Slicer 3D, and overestimation with Dolphin 3D and Invesalius. Excellent reliability was found for both intra-observer and inter-observer readings. Despite the excellent reliability, the present findings suggest that data of condylar morphology obtained with semi-automatic segmentation should be taken with caution when an accurate definition of condylar boundaries is required.

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Kubínová, Lucie, Xiao Wen Mao, and Jiří Janáček. "Blood Capillary Length Estimation from Three-Dimensional Microscopic Data by Image Analysis and Stereology." Microscopy and Microanalysis 19, no. 4 (May 14, 2013): 898–906. http://dx.doi.org/10.1017/s1431927613001487.

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AbstractStudies of the capillary bed characterized by its length or length density are relevant in many biomedical studies. A reliable assessment of capillary length from two-dimensional (2D), thin histological sections is a rather difficult task as it requires physical cutting of such sections in randomized directions. This is often technically demanding, inefficient, or outright impossible. However, if 3D image data of the microscopic structure under investigation are available, methods of length estimation that do not require randomized physical cutting of sections may be applied. Two different rat brain regions were optically sliced by confocal microscopy and resulting 3D images processed by three types of capillary length estimation methods: (1) stereological methods based on a computer generation of isotropic uniform random virtual test probes in 3D, either in the form of spatial grids of virtual “slicer” planes or spherical probes; (2) automatic method employing a digital version of the Crofton relations using the Euler characteristic of planar sections of the binary image; and (3) interactive “tracer” method for length measurement based on a manual delineation in 3D of the axes of capillary segments. The presented methods were compared in terms of their practical applicability, efficiency, and precision.

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Rizvi, Syed Saif Abbas, Jae Hwan Choi, Vakhtang Tchantchaleishvili, and Howard Todd Massey. "3D reconstruction of the heart and the LMCA aneurysm with fistula tract using 3D Slicer." ASVIDE 5 (June 2018): 589. http://dx.doi.org/10.21037/asvide.2018.589.

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Marbun, Frince, and Richard A. M. Napitupulu. "Desain dan Pembuatan Prototype Piston Honda MEGAPRO FI Menggunakan 3D Printing." SPROCKET JOURNAL OF MECHANICAL ENGINEERING 1, no. 2 (March 14, 2020): 81–91. http://dx.doi.org/10.36655/sprocket.v1i2.184.

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3D printing technology has great potential in today's manufacturing world, one of its uses is in making miniatures or prototypes of a product such as a piston. One of the most famous and inexpensive 3D printing (additive manufacturing) technologies is Fused Deposition Modeling (FDM), the principle FDM works by thermoplastic extrusion through a hot nozzle at melting temperature then the product is made layer by layer. The two most commonly used materials are ABS and PLA so it is very important to know the accuracy of product dimensions. FDM 3D Printing Technology is able to make duplicate products accurately using PLA material. FDM machines work by printing parts that have been designed by computer-aided design (CAD) and then exported in the form of STL or .stl files and uploaded to the slicer program to govern the printing press according to the design. Using Anet A8 brand 3D printing tools that are available to the public, Slicing of general CAD geometry files such as autocad and solidwork is the basis for making this object. This software is very important to facilitate the design process to be printed. Some examples of software that can be downloaded and used free of charge such as Repetier-Host and Cura. by changing the parameters in the slicer software is very influential in the 3D printing manufacturing process.

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Sumartono, Sumartono, Abdul Rasyid, Andi Maju Sinaga, Erika Putri Siahaan, Kiki Afrimanti Silalahi, and Yusuf Akuputra Purba. "TEKNOLOGI BODY DRONE DENGAN MENGGUNAKAN SOFTWARE CATIA DAN PRINTER 3 DIMENSI." SINERGI POLMED: Jurnal Ilmiah Teknik Mesin 3, no. 1 (April 5, 2022): 81–90. http://dx.doi.org/10.51510/sinergipolmed.v3i1.658.

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Perkembangan ilmu pengetahuan dan teknologi mengalami peningkatan yang sangat pesat. Pada era sekarang ini memasuki sektor revolusi industri 4.0 terutama di bidang manufaktur. Printer 3D merupakan salah satu terobosan inovasi terbaru dalam dunia teknologi sesuai dengan era revolusi industri 4.0 yang menggunakan teknologi rapid prototyping yang berasal dari data software desain CAD/CAM dengan metode menumpuk bahan lapisan demi lapisan. Unmanned Aerial Vehicle (UAV) atau disebut juga drone merupakan pesawat terbang tidak berawak yang merupakan inovasi yang lahir dari kemajuan teknologi. Harga komponen pada drone beragam mulai dari terendah hingga tertinggi, tergantung dari jenis drone yang digunakan. Tujuan dari perancangan ini adalah untuk membuat body drone menggunakan printer 3D dengan harga yang lebih murah. Dalam pembuatan desain body drone, menggunakan software Catia dan software Creality Slicer untuk proses perintah lanjutan pada printer 3D. Pengaturan parameter menu basic dan advanced pada software Creality Slicer untuk menentukan kualitas dan waktu proses printing, pemilihan parameter dilakukan dengan cara mencetak desain dalam berbagai opsi sebelum mendapatkan hasil yang sesuai. Hasil print body drone menggunakan printer 3D dibandingkan dengan body bawaan dari drone dan dilakukan pengujian terbang. Hasil akhir dari perancangan ini adalah body drone yang terbuat dari material PLA ( Polylactic Acid ) dengan total waktu proses printing 18 jam 49 menit, massa 63 gram, waktu terbang 16 menit 7 detik

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Amaliya, R., St Aisyah, A. P. Hariyanto, F. Jannah, A. Sarasechan, Nasori, A. Rubiyanto, M. Haekal, Endarko, and A. Nainggolan. "Analysis study of doses distribution in lung cancer using 3D Slicer." Journal of Physics: Conference Series 1943, no. 1 (July 1, 2021): 012047. http://dx.doi.org/10.1088/1742-6596/1943/1/012047.

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Salavert-Torres, José, Andrii Iudin, Ingvar Lagerstedt, Eduardo Sanz-García, Gerard J. Kleywegt, and Ardan Patwardhan. "Web-based volume slicer for 3D electron-microscopy data from EMDB." Journal of Structural Biology 194, no. 2 (May 2016): 164–70. http://dx.doi.org/10.1016/j.jsb.2016.02.012.

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Šljivic, M., A. Pavlovic, M. Kraišnik, and J. Ilić. "Comparing the accuracy of 3D slicer software in printed enduse parts." IOP Conference Series: Materials Science and Engineering 659 (October 31, 2019): 012082. http://dx.doi.org/10.1088/1757-899x/659/1/012082.

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Zhang, Fan, Thomas Noh, Parikshit Juvekar, Sarah F. Frisken, Laura Rigolo, Isaiah Norton, Tina Kapur, et al. "SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization." JCO Clinical Cancer Informatics, no. 4 (September 2020): 299–309. http://dx.doi.org/10.1200/cci.19.00141.

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PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

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Kholodilov, A. A., E. V. Faleeva, and M. V. Kholodilova. "Development of Software for the Implementation of an Adaptive Approach to the Generation of Internal Filling of a 3D Model in Additive Manufacturing." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012176. http://dx.doi.org/10.1088/1742-6596/2096/1/012176.

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Abstract The article describes the technology of software development for the implementation of an adaptive approach to generating the external and internal structure of a 3D model in preparation for 3D printing when transferring from *.stl format to * g.code format. The main stages of software design, the principles of mathematical modeling underlying the development are considered, the practical significance of the developed software complex-slicer is shown with a description of the functionality and conceptual development.

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Oyama, R., M. Jakab, A. Kikuchi, T. Sugiyama, R. Kikinis, and S. Pujol. "Towards improved ultrasound-based analysis and 3D visualization of the fetal brain using the 3D Slicer." Ultrasound in Obstetrics & Gynecology 42, no. 5 (October 2, 2013): 609–10. http://dx.doi.org/10.1002/uog.12484.

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Zhang, Danfeng, Jigang Chen, Kaiwei Han, Mingkun Yu, and Lijun Hou. "Management of Penetrating Skull Base Injury: A Single Institutional Experience and Review of the Literature." BioMed Research International 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/2838167.

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Background. Penetrating skull base injury (PSBI) is uncommon among head injuries, presenting unique diagnostic and therapeutic challenges. Although many cases of PSBIs have been reported, comprehensive understanding of its initial diagnosis, management, and outcome is still unavailable. Materials and Methods. A retrospective review was performed for patients treated in neurosurgical department of Changzheng Hospital for PSBIs. Presurgical three-dimensional (3D) Slicer-assisted reconstructions were conducted for each patient. Then we reviewed previous literature about all the published cases of PSBIs worldwide and discussed their common features. Results. A total of 5 patients suffering PSBIs were identified. Penetrating points as well as the surrounding neurovascular structures were clearly visualized, assisting in the presurgical planning of optimal surgical approach and avoiding unexpected vascular injury. Four patients underwent craniotomy with foreign bodies removed successfully and 1 patient received conservative treatment. All of them presented good outcomes after proper management. Conclusion. Careful physical examination and radiological evaluation are essential before operation, and angiography is recommended for those with suspected vascular injuries. 3D modeling with 3D Slicer is practicable and reliable, facilitating the diagnosis and presurgical planning. Treatment decision should be made upon the comprehensive evaluation of patient’s clinicoradiological features and characteristics of foreign bodies.

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Alexander, K. M., C. Pinter, J. Andrea, G. Fichtinger, and L. J. Schreiner. "Implementation of an efficient workflow process for gel dosimetry using 3D Slicer." Journal of Physics: Conference Series 573 (January 12, 2015): 012042. http://dx.doi.org/10.1088/1742-6596/573/1/012042.

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