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Meshmixer

Manufactured by Autodesk
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Sourced in United States, Canada, France

Meshmixer is a free 3D editing software developed by Autodesk. It allows users to import, edit, and manipulate 3D models. The software provides tools for mesh repair, sculpting, and boolean operations. Meshmixer supports a variety of 3D file formats and is intended for hobbyists, makers, and 3D printing enthusiasts.

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Lab products found in correlation

Fusion 360, by Autodesk (4 mentions) Simvascular, by Autodesk (3 mentions) Osirix, by Pixmeo (2 mentions) Optima ct660, by GE Healthcare (2 mentions) Trios 3 scanner, by 3Shape (2 mentions) Form 3, by Formlabs (2 mentions) Velocity ai software, by Agilent Technologies (1 mentions) Methanol, by Merck Group (1 mentions) Geomagic control, by 3D Systems (1 mentions) Magnetom skyra, by Siemens (1 mentions) Alginoplast, by Kulzer (1 mentions) Matlab, by MathWorks (1 mentions) Meshmixer version 3, by Autodesk (1 mentions) Prusaslicer, by Prusa Research (1 mentions) Catia, by Dassault Systèmes (1 mentions) Preform software, by Formlabs (1 mentions) Cerec omnicam, by Dentsply (1 mentions) Solidworks 2019, by Dassault Systèmes (1 mentions) Photoscan, by Agisoft (1 mentions) Objet260 connex3 printer, by Stratasys (1 mentions)

111 protocols using meshmixer

1

Radiological Analysis of Shoulder Instability

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CT scan DICOM (Digital Imaging and Communications in Medicine) files were obtained through the radiology archiving system of the hospitals. Different CT scanners were used up to 120-140 Kv and 500-700 mA. For measurement of the location of the Hill-Sachs and the involvement of the rotator cuff footprint, we evaluated the DICOM files using RadiAnt DICOM viewer (Medixant, Poznan, Poland; RadiAnt DICOM Viewer [Software] URL: https://www.radiantviewer.com). We used OsiriX DICOM Viewer15 (link) to render a 3-dimensional model of the CT scans and analyzed the glenoid track with Meshmixer (Autodesk Inc., San Rafael, CA, USA; Meshmixer.com">www.Meshmixer.com).
Alkaduhimi H., van der Woude H.J., Verweij L.P., Janssen S.J., Willigenburg N.W., Chen N, & van den Bekerom M.P. (2022). Greater tuberosity fractures are not a continuation of Hill-Sachs lesions, but do they have a similar etiology?. JSES International, 6(3), 396-400.
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2

Patient-Specific Mandibular Splint Design

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The patient-specific CPD was designed with the open-source software Meshmixer (Autodesk, San Francisco, CA, USA). The operation was planned virtually with IPS CaseDesigner (KLS Martin Group, Tuttlingen, Germany), in which the STL files of the operated jaw were generated and subsequently exported. The final splint and the operated mandible were imported to Meshmixer (Autodesk, San Francisco, CA, USA). With preformed tools, an additional wing was attached to each side of the final splint. The surface of the ascending ramus was imprinted using a Boolean function. The modified splint thus reflected the planned position of the ramus (Figure 2).
Straub A., Gubik S., Kübler A., Breitenbuecher N., Vollmer A., Renner T., Müller-Richter U., Hartmann S, & Brands R. (2024). Comparison of Patient-Specific Condylar Positioning Devices and Manual Methods in Orthognathic Surgery: A Prospective Randomized Trial. Journal of Clinical Medicine, 13(3), 737.
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3

3D-Printing Neurovascular Phantoms

Cited 1 time
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Segmentations were exported as Stereolithographic (STL) files. STL files were loaded into Autodesk Meshmixer where they were prepared for 3D printing. Each mesh was manipulated to smooth the geometry, thicken the vessel walls, add inserts for pressure sensors, and add a supportive base using a method previously described by Sommer et. al. [5 (link), 20 ]. Thickening of the vessel walls kept the inner lumen unchanged and smoothing helped remove artifacts of the original CT image. In addition to the neurovascular phantoms and with the same methods, a one-way aortic arch was segmented in Vitrea and manipulated in Autodesk Meshmixer for 3D-printing. Each neurovascular phantom was then 3D printed using a Stratasys Eden 260V printer (Eden Prairie, MN) and Tango+ material, a soft rubber-like polymer that mimics the compliance of healthy vasculature. The generic aortic arch was additively printed with polylactic acid (PLA) filament using a Creality Ender 5 3D-printer.
Paccione E, & Ionita C.N. (2021). Challenges in hemodynamics assessment in complex neurovascular geometries using computational fluid dynamics and benchtop flow simulation in 3D printed patient specific phantoms. Proceedings of SPIE--the International Society for Optical Engineering, 11600, 116000Z.
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4

Atrial Fibrillation Patients' Electrophysiology

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The electrical recordings from 25 atrial fibrillation patients from Hospital Gregorio Marañón, Madrid, Spain (Ethics Committee Approval 475/14) described elsewhere (Rodrigo et al., 2020 (link); Molero et al., 2021 (link)) were used. To record the signals 57 electrodes distributed on the torso of the patients were employed. The atrial geometries were also obtained from the same patients using Magnetic Resonance Imaging, and the 3D models were segmented through ITK-Snap (Yushkevich et al., 2006 (link)) and Autodesk Meshmixer (Schmidt and Singh, 2010 (link)). Furthermore, the torso models were obtained from photogrammetry, and 3D geometries consisting of triangular meshes were constructed (Remondino, 2004 (link)) and refined with Autodesk Meshmixer.
Molero R., González-Ascaso A., Hernández-Romero I., Lundback-Mompó D., Climent A.M, & Guillem M.S. (2022). Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation. Frontiers in Physiology, 13, 908364.
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5

3D Model Post-Processing for Anatomical Visualization

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Secondly, Autodesk® Meshmixer TM was used to process the model further. The model was first checked for integrity, watertightness and manifold using Analysis–Inspector–Auto Repair All (Fig. 1e). Next, it was checked for any visible imperfections like holes, spikes, and unwanted connections between parts. These artifacts were manually repaired using selective editing (Select–Discard–Select–Erase and Fill) and sculpting tools (Sculpt–Brushes–Robust Smooth). These steps were crucial to the entire process and required familiarity with regional anatomy, therefore they were done by a clinician. Additionally, the model inlet and outlet were cut straight (Edit–Plane Cut) if they were found to be uneven. Finally, the model was re-meshed and reduced to 300,000–2,000,000 triangles and repaired again using the Analysis–Inspector tool. After careful inspection of the final images (Fig. 1f), the model was exported to an OBJ file.
Tretiakow D., Tesch K., Meyer-Szary J., Markiet K, & Skorek A. (2020). Three-dimensional modeling and automatic analysis of the human nasal cavity and paranasal sinuses using the computational fluid dynamics method. European Archives of Oto-Rhino-Laryngology, 278(5), 1443-1453.
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6

3D Modeling of Nasal Anatomy

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Two authors (a surgeon familiar with nasal CT scans and anatomy (DT) and a physician with over 3 years of expertise in image segmentation and 3-D medical printing (JMS) closely cooperated to develop and optimize the segmentation process. Image processing and modeling of the human nasal cavity and paranasal sinuses were performed using open-source and freeware software. 3D Slicer (version 4.10.2) was used primarily for segmentation and new surface model generation. Further processing was done using Autodesk® Meshmixer TM (version 3.5.474).
Tretiakow D., Tesch K., Meyer-Szary J., Markiet K, & Skorek A. (2020). Three-dimensional modeling and automatic analysis of the human nasal cavity and paranasal sinuses using the computational fluid dynamics method. European Archives of Oto-Rhino-Laryngology, 278(5), 1443-1453.
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7

Mesh-Mixing: Optimizing 3D File Quality

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Mesh-mixing refers to any final edits on the reconstructed 3D file. We found the freeware Meshmixer (from Autodesk, California, United States) to be the most useful for this purpose. Unwanted segments from the 3D file can be easily deleted. Features on the 3D file can either be emphasised or masked. Erroneous print areas can be corrected via the “Analysis” and then “Repair” tabs.
Lam K.Y., Mark C.W, & Yee S.Y. (2021). Office 3D-printing in paediatric orthopaedics: the orthopaedic surgeon’s guide. Translational Pediatrics, 10(3), 474-484.
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8

3D-Printed Prostate Molds for Histopathology

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Individually designed prostate molds were created for each patient to guide histopathology sectioning and to preserve the in-vivo shape and orientation of the specimen. A cubical shape was created in Fusion 360 (Autodesk, Inc. San Rafael, California, USA) (Fig. 1C). The cube had a standard volume of 6.6 × 6.6 × 6.6 cm3 and was constructed out of two separate parts that could be joined using a locking mechanism (Fig. 1D). Throughout the cube, eleven 1 mm thick slits were inserted (with 5 mm spacing), to be used for histopathology slicing. The exported RT-structs were converted into Standard Triangle Language (STL)-files using MICE toolkit (Nonpi Medical AB, Umeå, Sweden) [23] . The STL-files were imported into Meshmixer (Autodesk, Inc. San Rafael, California, USA) where they were smoothed and simplified by reducing the number of vertices (Fig. 1B) and subsequently used as inputs into Fusion 360 and subtracted from the cube. For each patient two molds were printed, with a margin of +1 mm and +2 mm, respectively, using MakerBot Replicator + 3D-printer (MakerBot Industries, Brooklyn, NY USA).
Sandgren K., Nilsson E., Keeratijarut Lindberg A., Strandberg S., Blomqvist L., Bergh A., Friedrich B., Axelsson J., Ögren M., Ögren M., Widmark A., Thellenberg Karlsson C., Söderkvist K., Riklund K., Jonsson J, & Nyholm T. (2021). Registration of histopathology to magnetic resonance imaging of prostate cancer. Physics and Imaging in Radiation Oncology, 18, 19-25.
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9

3D Specimen Scanning for Intraoperative Margin Communication

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In a subset of cases, 3D specimen processing was used concurrently with the standard FSA protocol to augment intraoperative communication of frozen section results from the pathology lab to the OR (Figure 2). While surgeons at our institution occasionally scrub out of the OR to orient the specimen with the pathologist; in cases of intraoperative CAD margin communication, the surgeon remained scrubbed in the OR throughout the case. Following 3D scanning image acquisition, the resultant 3D model of the en bloc resection specimen was exported in 3MF file format into a CAD workspace (Meshmixer, Autodesk Inc., San Rafael, CA).
Specimens were inked, sectioned, and frozen into hematoxylin and eosin (H&E) slides per standard FSA protocol, while a research team member (Kayvon F. Sharif or Michael C. Topf) concurrently delineated each anatomic site and type of margin (shave or perpendicular) onto the 3D model using a digital airbrush in the CAD workspace.
The CAD workspace with specimen map was displayed at the desk of the surgical pathology fellow and/or attending pathologist to provide anatomic context for each frozen section H&E slide produced. The CAD workspace was also displayed on overhead OR monitors via remote videoconferencing software (Zoom Video Communications, Inc., San Jose, CA) as a visual tool to facilitate communication of results from the pathologist to the surgeon.
Sharif K.F., Lewis JS J.r., Ely K.A., Mehrad M., Pruthi S., Netterville J.L., Rohde S.L., Langerman A., Mannion K., Sinard R.J., Rosenthal E.L, & Topf M.C. (2022). The computer-aided design margin: Ex vivo 3D specimen mapping to improve communication between surgeons and pathologists. Head & neck, 45(1), 22-31.
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10

Automated Dental Anatomy Segmentation

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The OS were mesh-wise annotated (teeth and gingiva) by different clinicians independently and in duplicate using the brush mode in Meshmixer (Autodesk, San Rafael, United States). Each triangle surface could only belong to one of the two classes. All segmented and labeled OS were subsequently reviewed and revised by two different clinicians (MH, DM). Each of the clinicians and reviewers was instructed and calibrated in the segmentation task using a standardized protocol before the annotation and reviewing process. The definitive dataset was constructed from all annotated meshes.
The training boxes were calculated based on the mesh-wise annotation. For each tooth in the OS, the training box is determined by computing the minimum 3D bounding box around the tooth’s points.
Vinayahalingam S., Kempers S., Schoep J., Hsu T.M., Moin D.A., van Ginneken B., Flügge T., Hanisch M, & Xi T. (2023). Intra-oral scan segmentation using deep learning. BMC Oral Health, 23, 643.
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