Relevant bibliographies by topics / 3D Slicer

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic '3D Slicer'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "3D Slicer"

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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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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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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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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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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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Dissertations / Theses on the topic "3D Slicer"

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Korčuška, Robert. "Segmentace tomografických dat v prostředí 3D Slicer." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220376.

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This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.
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Chalupa, Daniel. "Rozšiřující modul platformy 3D Slicer pro segmentaci tomografických obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316852.

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This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.
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Forbes, Jessica LeeAnn. "Development and verification of medical image analysis tools within the 3D slicer environment." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/3085.

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Rapid development of domain specialized medical imaging tools is essential for deploying medical imaging technologies to advance clinical research and clinical practice. This work describes the development process, deployment method, and evaluation of modules constructed within the 3D Slicer environment. These tools address critical problems encountered in four different clinical domains: quality control review of large repositories of medical images, rule-based automated label map cleaning, quantification of calcification in the heart using low-dose radiation scanning, and waist circumference measurement from abdominal scans. Each of these modules enables and accelerates clinical research by incorporating medical imaging technologies that minimize manual human effort. They are distributed within the multi-platform 3D Slicer Extension Manager environment for use in the computational environment most convenient to the clinician scientist.
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Gerhards, Christian. "Dreidimensionale Rekonstruktion eines mit Heidenhain-Woelcke-Lösung gefärbten Rhesusaffenhirns zur Darstellung der Myeloarchitektonik aus der Friedrich-Sanides-Sammlung." Aachen Mainz, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2758034&prov=M&dok_var=1&dok_ext=htm.

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Pizzini, David. "Use of alternative materials with fused deposition modeling 3D printing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Questa tesi di laurea si basa sul lavoro svolto durante due tirocini della durata di oltre 300 ore ciascuno nell’azienda Carpigiani S.r.l. sita in Anzola dell’Emilia. Nel primo capitolo si va ad analizzare rapidamente la storia dell’azienda fino ai giorni odierni e le principali tipologie di macchine che vengono prodotte, al fine di dimostrare l’effettiva utilità dell’additive manufacturing in una realtà che ha bisogno di re-innovarsi costantemente nonostante essa sia già leader mondiale da anni nella produzione di macchine per il gelato. Nel secondo capitolo, dopo un rapido excursus generale sull’additive manufacturing, si illustra brevemente il funzionamento ed i vantaggi della tecnologia di stampa 3D FDM (fused deposition modeling); in seguito, si descrive e si analizzano i principali vantaggi e problematiche della macchina German RepRapx350pro posseduta dall’azienda. In modo analogo per quanto riguarda la tecnologia di multijet printing e la stampante Projet 3510 SD. La richiesta di sintetizzare e schematizzare i vari tipi di errore è stata mossa direttamente dall’ufficio tecnico per motivare diversi tipi di scelte nei futuri acquisti di macchine per la prototipazione. Nel terzo capitolo, viene affrontata la scelta dei parametri di processo per due tipologie di materiali mai utilizzati prima in Carpigiani con la tecnologia FDM, ovvero un filamento misto in Policarbonato/ABS ed uno in Nylon, entrambi prodotti dalla Filoalfa, azienda italiana specializzata da qualche anno nella produzione di filamenti di alta qualità per la stampa 3D. Lo scopo è quello di creare un profilo di stampa con il software Ultimaker Cura per entrambi i materiali basandosi su indicazioni generali date dai fornitori, sui numerosissimi punti deboli della stampante e sul trial and error.
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Milli, Giorgia. "Uso di 3d slicer in ambito di ricerca clinica: Una revisione critica delle esperienze di riferimento." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9008/.

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Negli ultimi 20 anni il progresso tecnologico ha segnato un profondo cambiamento in svariati ambiti tra i quali quello della Sanità in cui hanno preso vita apparecchiature diagnostiche, cosiddette “digitali native”, come la Tomografia Computerizzata (TC), la Tomografia ad Emissione di Positroni (PET), la Risonanza Magnetica Nucleare (RMN), l’Ecografia. A differenza delle diagnostiche tradizionali, come ad esempio la Radiologia convenzionale, che forniscono come risultato di un esame un’immagine bidimensionale ricavata dalla semplice proiezione di una struttura anatomica indagata, questi nuovi sistemi sono in grado di generare scansioni tomografiche. Disporre di immagini digitali contenenti dati tridimensionali rappresenta un enorme passo in avanti per l’indagine diagnostica, ma per poterne estrapolare e sfruttare i preziosi contenuti informativi occorrono i giusti strumenti che, data la natura delle acquisizioni, vanno ricercati nel mondo dell’Informatica. A tal proposito il seguente elaborato si propone di presentare un software package per la visualizzazione, l’analisi e l’elaborazione di medical images chiamato 3D Slicer che rappresenta un potente strumento di cui potersi avvalere in differenti contesti medici. Nel primo capitolo verrà proposta un’introduzione al programma; Seguirà il secondo capitolo con una trattazione più tecnica in cui verranno approfondite alcune funzionalità basilari del software e altre più specifiche; Infine nel terzo capitolo verrà preso in esame un intervento di endoprotesica vascolare e come grazie al supporto di innovativi sistemi di navigazione chirurgica sia possibile avvalersi di 3D Slicer anche in ambiente intraoperatorio
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Rohozhyna, N., Ivan Samoilenko, and Ksenia Meleshko. "Procedure of editing existing polygonal models by SolidWorks system." Thesis, National aviation university, 2021. https://er.nau.edu.ua/handle/NAU/52829.

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1. 2021 Best 3D Printer Slicer Software [Electronic resource]. - Access mode: https://all3dp.com/1/best-3d-slicer-software-3d-printer/
The ability to use 3D printing technology directly depends on the quality of the input geometric model. It is usually difficult to see small defects, missing triangles, convex and other deformations of the grid in slicers. There are online services stlfixers that fix network models. They quickly fill in defects, but are limited by the size and number of triangles of the model. In this case, slicers "do not see" the smallest holes in the grid or one / two triangles that protrude beyond the shape of the part, such defects are simply not printed, so in most cases stlfixers are quite enough.
Можливість використання технології 3D-друку безпосередньо залежить від якості вхідної геометричної моделі. Зазвичай важко побачити дрібні дефекти, відсутні трикутники, опуклі та інші деформації сітки в нарізках. Існують онлайн-сервіси stl-fixers, які виправляють моделі. Вони швидко заповнюють дефекти, але обмежені розміром і кількістю трикутників моделі. У цьому випадку слайсери "не бачать" найменших отворів у сітці або одного / двох трикутників, які виступають за форму деталі, такі дефекти просто не друкуються, тому в більшості випадків stlfixers цілком достатньо.
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Bove, Christopher. "Constrained Motion Planning System for MRI-Guided, Needle-Based, Robotic Interventions." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/310.

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In needle-based surgical interventions, accurate alignment and insertion of the tool is paramount for providing proper treatment at a target site while minimizing healthy tissue damage. While manually-aligned interventions are well-established, robotics platforms promise to reduce procedure time, increase precision, and improve patient comfort and survival rates. Conducting interventions in an MRI scanner can provide real-time, closed-loop feedback for a robotics platform, improving its accuracy, yet the tight environment potentially impairs motion, and perceiving this limitation when planning a procedure can be challenging. This project developed a surgical workflow and software system for evaluating the workspace and planning the motions of a robotics platform within the confines of an MRI scanner. 3D Slicer, a medical imaging visualization and processing platform, provided a familiar and intuitive interface for operators to quickly plan procedures with the robotics platform over OpenIGTLink. Robotics tools such as ROS and MoveIt! were utilized to analyze the workspace of the robot within the patient and formulate the motion planning solution for positioning of the robot during surgical procedures. For this study, a 7 DOF robot arm designed for ultrasonic ablation of brain tumors was the targeted platform. The realized system successfully yielded prototype capabilities on the neurobot for conducting workspace analysis and motion planning, integrated systems using OpenIGTLink, provided an opportunity to evaluate current software packages, and informed future work towards production-grade medical software for MRI-guided, needle-based robotic interventions.
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Franzén, Johan. "FrankZlicer : Direct slicing using arcs." Thesis, Mittuniversitetet, Avdelningen för data- och systemvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36021.

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3D printing a CAD modelnormally requires conversion into a polygon mesh, usually an STL-file, in orderto be able to load the model in the slicer. This conversion destroys roundsurfaces and replaces them with flat surfaces. Slicing a polygon mesh resultsin one or more polygons, consisting of a number of straight lines. This canaffect both dimensional accuracy and surface smoothness. Modern 3D-printerscan, in addition to straight lines, handle arcs. However, today’s commonslicers can not generate arcs as the input does not contain any curvedfeatures. This project aims at finding an alternative solution. By directslicing of CAD models the slices can contain arcs, and the slicer can producearc commands for the 3D-printer. During this project a prototype slicer isconstructed as a proof of concept. The prototype handles STEP-files as inputand creates both linear and circular movement for the 3D-printer. The resultsshow that both the intermediate files (STEP/STL) and the resulting G-code filescan get smaller, yet preserving the original shape, by using this method. Theproposed solution has a positive effect on the 3D-printing workflow as well, asthe intermediate files can be imported back into the CAD system. The projectconcludes that there is possibly a bright future for direct slicing, but thereare more problems to solve before it can become reality.
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Monfared, Karlo. "3D printed food and customized siliconemolds : Investigating aesthetic appearance and food preparingmethods for a dysphagia diet." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42247.

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Dysphagia is a serious eating disorder currently affecting around 100 thousand people in Sweden and it’s estimated that 1 in 10 people will develop some form of dysphagia during their lifetime around the world. The condition occurs mainly in elders and people who’ve suffered a stroke. The disorder prohibits proper swallowing of food, impairing the ability to close airways and let food enter the proper pathways. Patients with dysphagia have to eat grounded food in order to not suffer from malnutrition. The food is usually served in timbales which are sometimes decorated but might not look as appealing or realistic as one might think. The repetitive and unrealistic food creates further appetite loss which may lead to malnutrition. This is the case amongst many elderly in home care and the complications maybe very serious indeed, leading to organ dysfunction, hospitalization and eventually death. The current timbales made at the kitchens for elderly care in Helsingborg are punched with a tool in rectangular, circular shapes and often decorated. A solution would be to increase the aesthetics of the served timbales and make them realistically looking. 3D printing offers numerous new opportunities for food production and may very well set a standard in the future for producing aesthetically pleasing food. Silicone molds may also provide the same, if not better results at the time being at a fraction of the price, sufficing to revert any negative impacts of the current timbale appearance. In the hopes of proving both concepts the work will initially be focused on preparing a broccoli but also applying one of the concepts on a cinnamon bun providing additional application. This report presents the progress of designing a realistic 3D broccoli model, product development of a realistic broccoli food mold, 3D scanning, 3D food printing and a literature study. The designed model could not be printed due to the printing gel’s incapability of supporting complex structures. Instead master shapes created for shaping the food mold were scanned, sliced and printed. The same masters were used as means for a second food mold. Both food molds did manage to cast realistic shapes with a broccoli puré and of a soft gelatine suspension. Material alternatives were evaluated as well as manufacturing possibilities. The designed broccoli and a refined scanned broccoli were both offered for free as means for further research and development.
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Book chapters on the topic "3D Slicer"

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Alexander, K. M., C. Pinter, J. Andrea, G. Fichtinger, and L. J. Schreiner. "3D Slicer Gel Dosimetry Analysis: Validation of the Calibration Process." In IFMBE Proceedings, 521–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_128.

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Rackerseder, Julia, Antonio Miguel Luque González, Charlotte Düwel, Nassir Navab, and Benjamin Frisch. "Fully Automated Multi-Modal Anatomic Atlas Generation Using 3D-Slicer." In Informatik aktuell, 306–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54345-0_69.

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Talukder, Sajedul. "GPU-based Medical Image Segmentation: Brain MRI Analysis Using 3D Slicer." In Artificial Intelligence Applications for Health Care, 109–21. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003241409-6.

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das Neves, Ana Maria Bender Seidenfuss, Luis Felipe Silva Toschi, Carlos Jader Feldman, and Michele Alberton Andrade. "Curvature Characterization of Cochlea Using CT-Based Ear Atlas and 3D Slicer Software." In XXVI Brazilian Congress on Biomedical Engineering, 149–53. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_23.

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Kikinis, Ron, Steve D. Pieper, and Kirby G. Vosburgh. "3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support." In Intraoperative Imaging and Image-Guided Therapy, 277–89. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7657-3_19.

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Nakanishi, Tomoko M. "3D Images." In Novel Plant Imaging and Analysis, 191–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4992-6_6.

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AbstractSpatial (3D) image construction from imaging plate (IP) images and the development of the microautography (MAR) method were presented. A rice grain was sliced every 5 μm, IP images were taken for successive slices, and the series of 2D images acquired by an IP were used to construct 3D images. In the case of 109Cd and 137Cs, the spatial distributions in the grain showed that the concentrations increased at the surface of the grain during the maturing process.
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Bianchi, Jonas, Beatriz Paniagua, Antonio Carlos De Oliveira Ruellas, Jean-Christophe Fillion-Robin, Juan C. Prietro, João Roberto Gonçalves, James Hoctor, et al. "3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research." In Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures, 44–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60946-7_5.

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Chen, Zhen, Yagang Wang, Zhe Li, Pei Yang, and Shizhang Hu. "A Novel Approach for Automatic Measurement of the Distal Femur Based on 3D Slicer Software." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 1395–402. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_143.

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Balkan, Aral, Josh Dura, Anthony Eden, Brian Monnone, James Dean Palmer, Jared Tarbell, and Todd Yard. "3D Slice Engine." In Flash 3D Cheats Most Wanted, 221–50. Berkeley, CA: Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-0814-3_8.

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Macari, Michael, and Gary Israel. "CT Image Acquisition: from Single Slice to Multislice." In 3D Image Processing, 11–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-59438-0_2.

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Conference papers on the topic "3D Slicer"

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Fang, Luping, Zhengjie Wan, Wenye Zeng, and Ping Cao. "Research on Internationalization of 3D Slicer." In 2013 International Conference on Computer Sciences and Applications (CSA). IEEE, 2013. http://dx.doi.org/10.1109/csa.2013.115.

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Zhang, Xipeng, Gang Xiong, Zhen Shen, Yiyao Zhao, Chao Guo, and Xisong Dong. "A GPU-based parallel slicer for 3D printing." In 2017 13th IEEE Conference on Automation Science and Engineering (CASE 2017). IEEE, 2017. http://dx.doi.org/10.1109/coase.2017.8256075.

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Uday, Patil Abhishek, Naiknimbalkar Digvijay, and J. B. Jeeva. "Pre-operative brain tumor segmentation using SLICER-3D." In 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). IEEE, 2014. http://dx.doi.org/10.1109/icgccee.2014.6922404.

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Hsieh, Hsien-Hsi, Yueh-Yi Lai, Wen-Kai Tai, and Sheng-Yi Chang. "A flexible 3D slicer for voxelization using graphics hardware." In the 3rd international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1101389.1101446.

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Bahkali, Ismail Mohammed, and Sudhanshu Kumar Semwal. "Multiple Ways for Medical Data Visualization Using 3D Slicer." In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00149.

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Poudel, Laxmi, Lucas Galvan Marques, Robert Austin Williams, Zachary Hyden, Pablo Guerra, Oliver Luke Fowler, Stephen Joe Moquin, Zhenghui Sha, and Wenchao Zhou. "Architecting the Cooperative 3D Printing System." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22711.

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Abstract Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple mobile 3D printing robots work together cooperatively to print the desired part. At the core of C3DP lies the chunk-based printing strategy. This strategy splits the desired part into smaller chunks, and then the chunks are assigned and scheduled to be printed by individual printing robots. In our previous work, we presented various hardware and software components of C3DP, such as mobile 3D printers, chunk-based slicing, scheduling, and simulation. In this study, we present a fully integrated and functional C3DP platform with all necessary components, including chunker, slicer, scheduler, printing robots, build floor, and outline how they work in unison from a system-level perspective. To realize C3DP, new developments of both hardware and software are presented, including new chunking approaches, scalable scheduler for multiple robots, SCARA-based printing robots, a mobile platform for transporting printing robots, modular floor tiles, and a charging station for the mobile platform. Finally, we demonstrate the capability of the system using two case studies. In these demonstrations, a CAD model of a part is fed to the chunker, divided into smaller chunks, passed to the scheduler, and assigned and scheduled to be printed by the scheduler with a given number of robots. The slicer generates G-code for each of the chunks and combines G-code into one file for each robot. The simulator then uses the G-code generated by the slicer to generate animations for visualization purposes.
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Kikinis, R., and S. Pieper. "3D Slicer as a tool for interactive brain tumor segmentation." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091765.

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Xu, Haiyong, H. Donald Gage, and Pete Santago. "An open source implementation of colon CAD in 3D slicer." In SPIE Medical Imaging, edited by Nico Karssemeijer and Ronald M. Summers. SPIE, 2010. http://dx.doi.org/10.1117/12.844370.

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Shin, Hwa Seon, Hye In Lee, and Euee Seon Jang. "An effective data structure for a 3D printing slicer API." In 2016 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2016. http://dx.doi.org/10.1109/icce-asia.2016.7804739.

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Choueib, Saleh, Csaba Pinter, Jean-Batiste Vimort, Andras Lasso, Jean-Christophe Fillion Robin, Ken Martin, and Gabor Fichtinger. "Evaluation of 3D slicer as a medical virtual reality visualization platform." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Baowei Fei and Cristian A. Linte. SPIE, 2019. http://dx.doi.org/10.1117/12.2513053.

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