|J Pathol Inform 2017,
Three-dimensional imaging and scanning: Current and future applications for pathology
Navid Farahani1, Alex Braun1, Dylan Jutt1, Todd Huffman1, Nick Reder2, Zheng Liu3, Yukako Yagi4, Liron Pantanowitz5
1 3Scan, Inc., San Francisco, California, USA
2 Department of Pathology, University of Washington, Seattle, Washington, USA
3 Department of Pathology, Saint Barnabas Medical Center, Livingston, New Jersey, USA
4 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
5 Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
|Date of Submission||14-Apr-2017|
|Date of Acceptance||03-Jul-2017|
|Date of Web Publication||07-Sep-2017|
2122, Bryant Street, San Francisco, CA 94110
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Imaging is vital for the assessment of physiologic and phenotypic details. In the past, biomedical imaging was heavily reliant on analog, low-throughput methods, which would produce two-dimensional images. However, newer, digital, and high-throughput three-dimensional (3D) imaging methods, which rely on computer vision and computer graphics, are transforming the way biomedical professionals practice. 3D imaging has been useful in diagnostic, prognostic, and therapeutic decision-making for the medical and biomedical professions. Herein, we summarize current imaging methods that enable optimal 3D histopathologic reconstruction: Scanning, 3D scanning, and whole slide imaging. Briefly mentioned are emerging platforms, which combine robotics, sectioning, and imaging in their pursuit to digitize and automate the entire microscopy workflow. Finally, both current and emerging 3D imaging methods are discussed in relation to current and future applications within the context of pathology.
Keywords: Computational pathology, three-dimensional imaging, three-dimensional reconstruction, three-dimensional scanning, volumetric histopathology
|How to cite this article:|
Farahani N, Braun A, Jutt D, Huffman T, Reder N, Liu Z, Yagi Y, Pantanowitz L. Three-dimensional imaging and scanning: Current and future applications for pathology. J Pathol Inform 2017;8:36
|How to cite this URL:|
Farahani N, Braun A, Jutt D, Huffman T, Reder N, Liu Z, Yagi Y, Pantanowitz L. Three-dimensional imaging and scanning: Current and future applications for pathology. J Pathol Inform [serial online] 2017 [cited 2022 Jun 26];8:36. Available from: https://www.jpathinformatics.org/text.asp?2017/8/1/36/214167
| Introduction|| |
Biomedical practitioners have leveraged a plethora of imaging tools to measure physiologic and phenotypic details. Many of these imaging modalities were highly manual and either very time consuming or cost prohibitive. For example, in the pioneering years of radiology practice, plain X-ray radiography employed time consuming, and arduous chemical processes to develop film-screen images. Today, medical professionals have a large arsenal of advanced imaging tools available. Such imaging tools rely on computer vision, algorithms, and graphics.
Imaging methods that dominate the biomedical field include ultrasonic evaluation, X-ray computed tomography (CT), positron emission tomography––CT (PET-CT), and magnetic resonance imaging (MRI). These methods are capable of producing sets of two-dimensional (2D) images and three-dimensional (3D) reconstructions for interpretation.,, 3D imagery provides a better way to visualize and accurately measure a patient's phenotypic characteristics., Within the context of pathology, volumetric display, and mesh reconstruction techniques are particularly alluring for examination of clinical tissue specimens. 3D imaging could also enhance the study of disease processes, especially those involving structural changes, and in which spatial relationships are relevant.,,
Currently, there is no clear “best” 3D imaging method, especially in regards to medical imaging. Rather, each of the available methods involves tradeoffs in image size, accuracy, and resolution. Thus, determining which 3D imaging method is most appropriate often depends on the medical question under investigation. Herein, we summarize current 3D imaging and 3D scanning methods, with an emphasis on techniques that enable 3D histopathologic reconstruction, such as serial 2D scanning, 3D scanning, and whole slide imaging (WSI). Emerging platforms that combine robotics, sectioning and imaging in their goal of digitizing, and automating the entire microscopy workflow are discussed. Future applications of current and novel 3D imaging methods within the context of pathology are also addressed.
| Three-Dimensional Scanning and Three-Dimensional Imaging|| |
It is important to review the semantic differences between 3D imaging and 3D scanning. In computer science, “image scanning,” often abbreviated to just “scanning,” describes the process by which a detector traverses an object, surface, or body part and uses electromagnetic radiation (EMR) to obtain images and convert them into a digital format. 3D scanners typically contain image sensors that capture light reflected off an object as pixel data. In this sense, an image is a 2D arrangement of pixels, which often corresponds to the resolution of the image sensor.
Laser technology was initially introduced in the 1960s. Following their invention, lasers were coupled with image sensors and used by computer vision software for image segmentation and reconstruction. Popular usage of the term “3D scanner” denotes a specific type of 3D laser scanner, which relies on nonionizing EMR, primarily visible light. In contrast to these 3D scanners, there are other 3D devices that employ high (X-ray, PET-CT) or low (radio, ultrasound) frequency EMR. The majority of this review is concerned with 3D scanners of the former type, as defined above since they are becoming increasingly popular, cheap, and relatively easy to use. However, before moving on to 3D scanners, it is worthwhile to review more conventional methods of 3D histopathologic analysis, like those offered by WSI platforms.
| Whole Slide Imaging|| |
Whole slide images are the digital equivalent of traditional glass slides and contain high-resolution representations of the same scanned material found on glass slides. In the past, WSI was mostly focused on 2D analysis at the expense of 3D structural analysis., More recently, 3D reconstruction of whole slide histological data has demonstrated value in the visualization and diagnosis of disease. High-resolution 3D histopathologic imagery is, especially advantageous in discovering diagnostic patterns, due to its improved correlation between imaging modalities such as MRI, conventional CT, and WSI.,,,
The WSI process begins with the creation of serial, glass slide-mounted tissue sections, obtained either through traditional or automated histology sectioning. Automated robotic microtomes, which automatically trim and section blocks, are particularly useful for 3D reconstruction of tissue sections. Benefits afforded by automated sectioning compared to traditional manual sectioning include the near-uniform thickness of sections, uniform orientation of sections (i.e., alignment of tissue between sections), and fewer sectioning artifacts., All these factors facilitate interpolation of structure between sections, resulting in high fidelity 3D reconstructions.
Serial sections are typically acquired at a thickness of 4–6 μ. They are then mounted on glass slides. While a minimum of fifty sections are recommended, an optimal 3D reconstruction is obtained with at least 100-200 serial sections. Such sections all need to be stained, using routine histologic and/or immunohistochemical techniques. Next, the stained glass slides need to be digitized using a WSI scanner, to generate a series of digital images, each corresponding to a different scanned level of the tissue block. These serial digital images are then run through commercially available or custom software, to generate 3D models [Figure 1]. Examples of WSI-compatible 3D reconstruction software include Voloom (microDimensions, Munich, Germany) and Image-Pro Premier 3D (Media Cybernetics, Rockville, MD, USA). In general, 3D reconstruction software involves the following steps: Registration, segmentation, interpolation, and volumetric rendering.
|Figure 1: Three-dimensional reconstruction of lung adenocarcinoma from serial two-dimensional whole slide images. H and E-stained glass slide-derived whole-slide images are run through commercially available or custom software, in order to generate three-dimensional models through registration, segmentation, interpolation and volumetric rendering or serial two-dimensional sections|
Click here to view
| Laser Scanning|| |
3D laser scanning, often abbreviated to just 3D scanning, has been used for reverse engineering and part inspection in the manufacturing industry, as well as a digital actor, prop, and set recreation in the visual effects industry. However, 3D scanners, like other emerging technologies, have experienced a dramatic decrease in equipment costs, making 3D scanning more accessible to a wider audience., 3D scanners analyze real-world objects to gather data on their shape and color. Data collected from the scanner is then used to construct a 3D mesh, which can be printed using various additive manufacturing (3D printing) methods. 3D models, which include 3D meshes, are best defined as “numerical description(s) of an object that can be used to render images of the object from arbitrary viewpoints and under arbitrary lighting conditions.” A list of common terminology, within the realm of 3D scanning, is provided in [Table 1].
| Three-Dimensional Reconstruction|| |
The 3D reconstruction process for laser scanners begins with the conversion of raw data elements into point clouds (or vertices) of geometric samples from the surface of the object, which are viewed and manipulated using graphics applications [Figure 2]a. A meshing process then takes place, whereby the vertices (points) in the point cloud are algorithmically connected to form a manifold surface called a mesh [Figure 2]b. That mesh is then generally stored as a series of components which define each polygon that make up its surface., At this point, the 3D model, which is typically a polygonal mesh, is further refined using one of several different 3D modeling applications. Next, images called textures are mapped onto the surface of the mesh in order to faithfully represent the original color of the object that was scanned [Figure 2]c. This is achieved by mapping each 3D vertex coordinate onto a corresponding coordinate within a 2D parametric (UV) unit plane. During the rendering process, this UV mapping is used to broadcast a 2D texture across the 3D surface of the model.
|Figure 2: Three-dimensional model generation pipeline through point clouds, meshes, and texture Maps for three-dimensional scanners. (a) For three-dimensional scanners, the reconstruction process begins with the conversion of the raw data elements into point clouds of geometric samples from the surface of the object. (b) A meshing process then takes place, whereby the points are algorithmically connected to form a manifold surface called a mesh. (c) Next, UV mapping is used to broadcast and map two-dimensional images, called textures, onto the surface of the three-dimensional polygonal mesh in order to faithfully represent the original color of the object scanned|
Click here to view
For many 3D scanners, multiple scans are required to produce a high fidelity and complete 3D representation of the object being scanned. Generally, in between scans, the object is oriented along a different axis, to ensure that point clouds are obtained from as many different directions as possible; this ensures that information is obtained from all sides of the object. After multiple scans are obtained, the individual scans are brought into a common reference system through a process that is usually referred to as alignment or registration. After the scans are registered, they are subsequently merged to create a more complete 3D model (i.e., more dense point cloud).
There are a wide range of potential approaches to 3D scanning, each with its own advantages and limitations. In this review, we mostly focus on nondestructive methods in which the object is left largely unaltered during digitization. Within this limited scope, 3D scanners fall into one of two broad categories: contact and noncontact data capture methods. [Figure 3] contains a broad classification scheme for both contact and noncontact 3D digitization methods. [Table 2] provides additional characteristics associated with commonly used noncontact, volumetric, and surface scanning methods.
|Figure 3: Classification of three-dimensional digitizing method. Three-dimensional scanners fall into broad contact and noncontact based categories. Contact three-dimensional scanners are sub-classified according to whether or not the scanning method is a destructive (i.e., knife-edge scanning microscopy, focused ion-beam scanning electron microscopy, etc.) or nondestructive process (i.e., coordinate measuring machine, articulate arms with encoders, etc.). Noncontact three-dimensional scanners are sub-classified by the type of electromagnetic radiation utilized. Within visible light-based noncontact scanning, methods can be further divided devices that emit or absorb radiation|
Click here to view
|Table 2: Characteristics of commonly used noncontact three-dimensional technologies|
Click here to view
| Contact Three-Dimensional Scanning|| |
Contact 3D scanners probe objects through physical touch, usually while the objects are mounted or laid upon a flat surface., The touching of the contact probe to various points on the surface of the object results in data capture. This method of data collection is generally more accurate for defining the geometric form of an object rather than organic freeform shapes. Mechanical contact-based digitizing is also more suitable for highly reflective, mirroring, or transparent objects and for objects with difficult-to-reach areas. Contact 3D scanners are, especially, useful for industrial reverse engineering applications when precision is the most important factor. Limitations of contact scanning include the relatively slow scan speed and the necessity for physical contact, which may modify or permanently damage the object. As mentioned above, contact 3D digitization requires physically interacting with the object, such that contact 3D scanners are further split into one of two subtypes: destructive and nondestructive [Figure 3]. Nondestructive scanners require physical touch but leave the object largely intact. Many popular, commercially available 3D scanners, especially those employed for industrial applications, are of the nondestructive type. An example of such would include a coordinate-measuring machine, which is commonly employed for reverse engineering, rapid prototyping, and large-scale part inspection. Destructive scanners, like automated serial block-face or serial section microscopy, produce volumetric data by consecutively removing minute layers of material, while digitizing each layer as it is processed. The process is repeated until the entire object has been fully digitized, and thus fully destroyed. Examples of destructive contact scanning include knife-edge scanning microscopy (KESM), micro-optical serial tomography, light-sheet microscopy, and focused-ion-beam scanning electron microscopy (FIBSEM). These platforms combine robotics, computer vision, and advanced optics for high-throughput imaging and computational analysis.,, For example, 3Scan's (San Francisco, CA, USA) commercial KESM platform [Figure 4] couples automated sectioning with light microscopy for imaging whole-mounted organs and large tissue volumes at speeds that are 1000 times faster than manual histology. These methods are popular among researchers in the medical and health sciences like connectomics, wherein high-resolution images are used to create structural maps of neural connections.,,
| Noncontact Three-Dimensional Scanning|| |
Noncontact methods offer a faster and more simple option for obtaining 3D scans. Since the 1980s, the optical (or light-based) noncontact scanners have become the preferred method for certain kinds of objects. Some include large, freeform, flexible or fragile objects, objects with numerous features, and objects where probe contact is not feasible (e.g., rare artifacts). Optical, noncontact 3D scanning is further divided into active and passive subtypes. For both subtypes, the concept is more or less the same. Light is reflected off an object's surface through an array of lenses and then onto an image sensor. Passive scanners illuminate objects using an undirected light source, such as ambient light. Passive scanning methods are simple to set up, have rapid measurement times, and some commercial versions provide automated surface matching. In contrast, active scanners employ a directed light source, such as lasers and light patterns. Computer's are able to calculate the 3D coordinates of points of an object's surface by comparing the image of an object light by directed light to what would have been captured under known conditions (i.e., no object). Active scanning is the more popular method.
Many commonly used noncontact, active 3D scanning microscopes use fluorescence imaging to provide contrast. Confocal, multi-photon, and light-sheet microscopy is often used in research laboratories to image small tissue samples at a limited depth. A fundamental challenge for currently available 3D fluorescence microscopy systems is the need to image large volumes of tissue at high resolution in a reasonable time frame. Confocal and multi-photon microscopy systems provide excellent resolution and contrast but can be prohibitively slow for imaging clinical specimens. For example, a recent study required 30 h to image a single kidney biopsy specimen. Light-sheet microscopy [Figure 5] offers superior speed of imaging compared to confocal and multi-photon microscopy systems while maintaining good resolution. These properties of light-sheet microscopy have led to high impact studies in neuroscience and developmental biology., However, current commercially available light-sheet microscopy systems are ill-suited to imaging larger clinical specimens, which is an area of active research.
|Figure 5: Three-dimensional light-sheet microscopy image of a prostate biopsy measuring (2 cm in length by 1 mm in diameter). The biopsy specimen was chemically cleared with 2, 2' thiodiethanol to enable three-dimensional imaging, then stained with DRAQ5 (nuclear) and eosin (cytoplasmic) fluorescent dyes. A custom-built light-sheet microscope imaged the biopsy in three-dimensions. The total time for clarification, staining, and imaging was <20 min. The nuclear and cytoplasmic channels were false-colored and volume rendered using Imaris software|
Click here to view
| File Formats|| |
Once an object is “captured” by a 3D scanner, it is turned into a 3D model through a computer-based reconstruction process. 3D modeling and computer-aided design software can be used for further modification of the model. There are three primary methods of 3D modeling: organic modeling, hard surface modeling, and procedural modeling [Table 3]. These models are then formatted in one of many 3D file types, some of which are compatible with 3D printers and commercial 3D printing vendors [Table 4]. It is important to note that only a few file formats will support the full gamut of geometry, colors, and textures. For example, the stereolithography format (.stl), which is arguably the most popular 3D file format for 3D printing, only supports geometric features. In addition, many commonly used 3D microscopy visualization software packages including Vaa3D  and ImageJ  use raster image formats such as tiff. These raster image formats are much more computationally expensive than the vector formats listed in [Table 4] and are not accelerated by the rapid advances in graphical processing units.
|Table 3: Various types of three-dimensional computer-aided design modeling|
Click here to view
| Practical Applications|| |
Research and clinical pathology both use 3D reconstruction of whole slide images. Recent clinical examples include classification of lung adenocarcinomas, diagnosis of colorectal pathologies from small biopsies, and metastasis of breast cancer to lymph nodes.,, Other applications include anatomical and micro-architectural features of normal tissue, tumor invasion, growth factor expression, and localization of therapeutic targets in relation to microvasculature., The reconstruction of whole slide images is hindered by digital artifacts from tissue sectioning and image capture, inconsistency of image qualities, and the arduous process of manual tissue sectioning. Serial tissue sectioning is the most significant obstacle due to the labor and time-intensive nature associated with optimizing the process of alignment of tissue sections. Due to these limitations, careful analysis of the cost to benefit is need when considering this method for scientific inquiry.
3D scanning is relatively prevalent across many nonmedical domains. High-end commercial scanners are used by archeologists and preservationists to acquire models of remains, historical artifacts and large excavations.,, Aerospace, mechanical, and structural engineering sectors rely on 3D scanners to document structural dimensions, monitor structural deformations, and for reverse engineering of objects.,, Industrial manufacturing utilizes 3D scanning for quality assurance and inspection.,, 3D scanning has also been a major part of the visual effects and gaming industry for over 20 years. In the medical field, 3D scanners are also used for several reasons, including the modeling of intricate anatomical structures, planning of complex surgical procedures, custom fabrication of medical devices, and the diagnosis of rare medical conditions.,,,,,,,,,,,,
However, 3D scanners are currently nearly absent in anatomic and clinical pathology. An exception to this would be their use in forensic pathology, for the documentation of specific injuries and as means of virtual autopsy., Virtual autopsies (virtopsy) routinely combine surface (i.e., photogrammetric) and volumetric (i.e., CT or MRI) scans as a means of examining deceased tissues in a digital environment. Recent investigations into the use of 3D printing in anatomic pathology have driven the use of 3D scanners for gross surgical specimen capture. 3D scanners for automated capture of gross surgical specimens is potentially feasible for digital archiving of specimens (e.g., in the laboratory information system), telepathology, education, medicolegal documentation, and experimental research.
Presumably, 3D scanning of gross surgical pathology specimens can reproduce realistic models of pathologic entities. These models can be used in medical training, clinical research, education, and clinicopathological correlation at multidisciplinary conferences. Furthermore, the application of 3D scanning techniques need not be confined to the macro level. Destructive 3D scanning of entire tissue blocks, through KESM, serial block face scanning-electron microscopy and FIBSEM, can also provide microscopic and ultrastructural 3D models of patient specimens. Datasets of varying levels of resolution, from sub-millimeter radiographic studies to sub-micron pathologic investigations, can be combined and rendered into an integrated, fully-comprehensive 3D model. These models would undoubtedly prove useful for many processes including tumor staging, margin assessment, pathologic-radiologic correlation, macro-microscopic correlation, and better insights into disease processes.
| Conclusion|| |
3D scanning and 3D imaging are emerging disruptive technologies. Their broad range of applications has the potential to expand into pathology practice. Driven by technological advances these tools continue to get cheaper, smaller, more reliable, and easier to use. Future 3D scanners will benefit from significant gains in scan rates. Currently, low scan rates represent a major technical bottleneck for many low-end desktop and handheld 3D scanners. Recently, Kadambi et al. described an innovative method of 3D scanning using high-quality depth sensing with polarization cues. Their findings allow for the creation of high-resolution 3D images, from only a sparse number of 2D pictures, taken by cameras with polarized lenses. Furthermore, the resolution of the images produced by this form of 3D scanning is much higher than that produced by high-precision laser scanners. While still in development, techniques like polarized 3D scanning will inevitably usher in the higher-resolution 3D models that can be acquired rapidly and cost effectively. Related technologies are also materializing and poised to profoundly change how we view and interact with 3D data. Chief among them are virtual and augmented reality wearable headsets and controllers (e.g., Oculus rift, HoloLens).
| Supplementary|| |
Downloadable 3D image of murine vasculature from the forebrain, created using the KESM platform: https://sketchfab.com/models/9757aed6ddf14265aaf94f936086c372.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Udupa JK, Herman GT. 3D Imaging in Medicine. 2nd
ed. Philadelphia: Pennsylvania; CRC Press; 1999.
Kim M, Huh KH, Yi WJ, Heo MS, Lee SS, Choi SC, et al.
Evaluation of accuracy of 3D reconstruction images using multi-detector CT and cone-beam CT. Imaging Sci Dent 2012;42:25-33.
Periago D, Scarfe W, Moshiri M, Scheetz J, Silveira A, et al
. Linear Accuracy and Reliability of Cone Beam CT Derived 3-Dimensional Images Constructed Using an Orthodontic Volumetric Rendering Program. The Angle Orthodontist. 2008;78- 3: 387-95.
Funato K, Kazuo F, Noriko H, Hidehiko N, Chiyoharu H. Applications of 3D Body Scanning Technology to Human Anthropometry: Body Surface Area and Body Volume Measurements in the Fields of Health and Sports Sciences. Proceedings of the 1st
Asian Workshop on 3D Body Scanning Technologies, Tokyo, Japan, 17-18 April, 2012; 2012.
Hirschmann MT, Wagner CR, Helmut R, Johann H. Standardized volumetric 3D-analysis of SPECT/CT imaging in orthopaedics: Overcoming the limitations of qualitative 2D analysis. BMC Med Imaging 2012;12:5.
Paish EC, Green AR, Rakha EA, Macmillan RD, Maddison JR, Ellis IO, et al.
Three-dimensional reconstruction of sentinel lymph nodes with metastatic breast cancer indicates three distinct patterns of tumour growth. J Clin Pathol 2009;62:617-23.
Kalinski T, Zwönitzer R, Sel S, Evert M, Guenther T, Hofmann H, et al
. Virtual 3D microscopy using multiplane whole slide images in diagnostic pathology. Am J Clin Pathol 2008;130:259-64.
Roberts N, Magee D, Song Y, Brabazon K, Shires M, Crellin D, et al
. Toward routine use of 3D histopathology as a research tool. Am J Pathol 2012;180:1835-42.
Farahani N, Parwani AV, Pantanowitz L. Whole slide imaging in pathology: Advantages, limitations, and emerging perspectives. Pathol Lab Med Int. 2015; 7: 23-33.
Liang Y, Wang F, Treanor D, Magee D, Teodoro G, Zhu Y, et al.
Liver whole slide image analysis for 3D vessel reconstruction. Proc IEEE Int Symp Biomed Imaging 2015;2015:182-85.
Foran DJ, Chen W, Yang L. Automated image interpretation computer-assisted diagnostics. Anal Cell Pathol (Amst) 2011;34:279-300.
Kong J, Cooper LA, Wang F, Gao J, Teodoro G, Scarpace L, et al.
Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. PLoS One 2013;8:e81049.
Fónyad L, Shinoda K, Farkash EA, Groher M, Sebastian DP, Szász AM, et al.
3-dimensional digital reconstruction of the murine coronary system for the evaluation of chronic allograft vasculopathy. Diagn Pathol 2015;10:16.
Ohnishi T, Takashi O, Takuya T, Yuka N, Noriaki H, Hideaki H, et al
. Connection and deformation of pathological images via a macro image for comparing different modality images of brain tumor. Anal Cell Pathol 2014;2014:1-3.
Nakamura Y, Yuka N, Takuya T, Takashi O, Noriaki H, Hideaki H, et al
. Registration between pathological image and MR image for comparing different modality images of brain tumor. Anal Cell Pathol 2014;2014:1-3.
Goubran M, de Ribaupierre S, Hammond RR, Currie C, Burneo JG, Parrent AG, et al.
Registration of in-vivo
MRI of surgically resected specimens: A pipeline for histology to in-vivo
registration. J Neurosci Methods 2015;241:53-65.
Sengle G, Tufa SF, Sakai LY, Zulliger MA, Keene DR. A correlative method for imaging identical regions of samples by Micro-CT, light microscopy, and electron microscopy: Imaging adipose tissue in a model system. J Histochem Cytochem 2012;61:263-71.
Onozato ML, Hammond S, Merren M, Yagi Y. Evaluation of a completely automated tissue-sectioning machine for paraffin blocks. J Clin Pathol 2013;66:151-4.
Senter-Zapata M, Patel K, Bautista PA, Griffin M, Michaelson J, Yagi Y, et al.
The role of micro-CT in 3D histology imaging. Pathobiology 2016;83:140-7.
Del Bimbo A, Pietro P. Content-based Retrieval of 3D Models. ACM Trans Multilmed Comput Commun Appl. 2006;2:20-43.
Várady T, Tamás V, Martin RR, Jordan C. Reverse engineering of geometric models – An introduction. Comput Aided Des Appl 1997;29:255-68.
Bernardini F, Fausto B, Holly R. The 3D Model Acquisition Pipeline. Comput Graph Forum 2002;21:149-72.
McHenry K. An Overview of 3D Data Content, File Formats and Viewers. Report No: NCSA-ISDA-2008-002. National Center for Supercomputing Applications; 2008.
Large A, Heritage G, Charlton M. Laser Scanning: The Future, in Laser Scanning for the Environmental Sciences. 1st ed. Oxford, UK:Wiley-Blackwell; 2009.
Curless B, Brian C. From range scans to 3D models. Vol. 33. ACM SIGGRAPH Computer Graphics; 1999. p. 38-41.
Ukida H, Hiroyuki U. 3D Shape and Specular Reflection Measurement Using Image Scanner. 2008 SICE Annual Conference; 2008.
Mitbauerová A, Čapek L, Ogawa R. A method of scar evaluation using non-contact 3D scanner. Comput Methods Biomech Biomed Engin 2013;16:302-4.
Luebke K, Karsten L. Coordinate Measuring Machine. CIRP Encyclopedia of Production Engineering. 2nd ed. Berlin, Germany: Springer International Publishing AG; 2014.
Mayerich D, Abbott L, McCormick B. Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain. J Microsc. 2008;231:134-43.
Osten P, Margrie TW. Mapping brain circuitry with a light microscope. Nat Methods 2013;10:515-23.
Mikula S, Denk W. High-resolution whole-brain staining for electron microscopic circuit reconstruction. Nat Methods 2015;12:541-6.
Lichtman JW, Pfister H, Shavit N. The big data challenges of connectomics. Nat Neurosci 2014;17:1448-54.
Lee WC, Bonin V, Reed M, Graham BJ, Hood G, Glattfelder K, et al
. Anatomy and function of an excitatory network in the visual cortex. Nature 2016;532:370-4.
Economo MN, Clack NG, Lavis LD, Gerfen CR, Svoboda K, Myers EW, et al.
Aplatform for brain-wide imaging and reconstruction of individual neurons. Elife 2016;5:e10566.
Breuckmann B. 25 Years of High Definition 3D Scanning: History, State of the Art, Outlook. EVA Proceedings. 2014: 262-66.
Olson E, Levene MJ, Torres R. Multiphoton microscopy with clearing for three dimensional histology of kidney biopsies. Biomed Opt Express 2016;7:3089-96.
Keller PJ, Schmidt AD, Wittbrodt J, Stelzer EH. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 2008;322:1065-9.
Dodt HU, Leischner U, Schierloh A, Jährling N, Mauch CP, Deininger K, et al.
Ultramicroscopy: Three-dimensional visualization of neuronal networks in the whole mouse brain. Nat Methods 2007;4:331-6.
Chen JX. 3D file formats. Guide to Graphics Software Tools. 2nd ed. London, UK: Springer-Verlag; 2009.
Peng H, Bria A, Zhou Z, Iannello G, Long F. Extensible visualization and analysis for multidimensional images using vaa3D. Nat Protoc 2014;9:193-208.
Schindelin J, Rueden CT, Hiner MC, Eliceiri KW. The imageJ ecosystem: An open platform for biomedical image analysis. Mol Reprod Dev 2015;82:518-29.
Park S, Pantanowitz L, Parwani AV. Digital imaging in pathology. Clin Lab Med 2012;32:557-84.
Onozato ML, Klepeis VE, Yagi Y, Mino-Kenudson M. A role of three-dimensional (3D)-reconstruction in the classification of lung adenocarcinoma. Anal Cell Pathol (Amst). 2012;35:79-84.
Wu ML, Varga VS, Kamaras V, Ficsor L, Tagscherer A, Tulassay Z, et al.
Three-dimensional virtual microscopy of colorectal biopsies. Arch Pathol Lab Med 2005;129:507-10.
Kaufman MH, Brune RM, Davidson DR, Baldock RA. Computer-generated three-dimensional reconstructions of serially sectioned mouse embryos. J Anat 1998;193(Pt 3):323-36.
Chang YM, Lu NH, Wu TC. Application of 3D laser scanning technology in historical building preservation: a case study of a Chinese temple. Proceedings of the SPIE 2005; 5875: 291-98.
Kersten TP, Hinrichsen N, Lindstaedt M, Weber C, Schreyer K, et al
. Architectural historical 4D documentation of the old-segeberg town house by photogrammetry, terrestrial laser scanning and historical analysis. Lecture Notes in Computer Science. Vol.8740. Berlin, Germany: Springer-Verlag; 2014.
Kersten TP, Lindstaedt M. Virtual architectural 3D model of the imperial cathedral (Kaiserdom) of Konigslutter, Germany through terrestrial laser scanning. Lecture Notes in Computer Science. Vol. 7616. Berlin, Germany: Springer;2012.
Yao AW. Applications of 3D scanning and reverse engineering techniques for quality control of quick response products. Int J Adv Manuf Technol 2004;26:1284-8.
Dantsker OD. Determining Aerodynamic Characteristics of an Unmanned Aerial Vehicle using a 3D Scanning Technique. 53rd
AIAA Aerospace Sciences Meeting; 2015.
de Wild M, Schollbach T, Schumacher R, Schkommodau E, Bormann T. Effects of laser parameters and scanning strategy on structural and mechanical properties of 3D NiTi implants fabricated with selective laser melting. Biomed Tech 2013: 58 (Suppl. 1)
McAtee S, Steven M, Michelle D, Romesh N. Simulation scan comparison for process monitoring using 3D scanning in manufacturing environments. Int J Adv Manuf Technol 2014;74:823-34.
Reinhart G, Tekouo W. Automatic programming of robot-mounted 3D optical scanning devices to easily measure parts in high-variant assembly. CIRP Annals - Manufacturing Technology. Vol. 58. Amsterdam, Netherlands: Elsevier; 2009. p.25-8.
Dodgson NA. Going to the Movies: Lessons from the film industry for 3D libraries. Lecture Notes in Computer Science. Vol. 8355. Berlin, Germany: Springer-Verlag; 2014.
Yusuf M, Chen B, Robinson I. Future Prospects of 3D Human Chromosome Imaging by Serial Block Face Scanning Electron Microscopy. Single Cell Biol 5:134.
Lloyd T, Timothy L, Mustafa SF, Cronin AJ. The role of cone beam CT and 3D facial soft tissue scanning in the assessment, planning and continued evaluation of patients undergoing orthognathic surgery. Br J Oral Maxillofac Surg 2008;46:e63.
Mehra P, Miner J, D'Innocenzo R, Nadershah M. Use of 3-D Stereolithographic models in oral and maxillofacial surgery. J Maxillofac Oral Surg 2011;10:6-13.
Bottino A, Andrea B, De Simone M, Aldo L. A Computer-Aided Technique for Planning Plastic Surgery Based on 3D Face Scans: Preliminary Results. Proceedings of the 1st
International Conference on 3D Body Scanning Technologies, Lugano, Switzerland; 19-20 October, 2010.
Fang C, Fang Z, Fan Y, Li J, Xiang F, Tao H, et al.
Application of 3D visualization, 3D printing and 3D laparoscopy in the diagnosis and surgical treatment of hepatic tumors. Nan Fang Yi Ke Da Xue Xue Bao 2015;35:639-45.
Dombroski CE, Balsdon ME, Adam F. A Low Cost 3D Scanning and Printing Tool for Clinical Use in the Casting and Manufacture of Custom Foot Orthoses. Proceedings of the 5th
International Conference on 3D Body Scanning Technologies, Lugano, Switzerland; 21-22 October, 2014.
Chromy A. Application of high-resolution 3D scanning in medical volumetry. Intl J Electron Telecomm 2016; 62:22-31.
Rajon DA, Bova FJ, Chi YY, Friedman WA. Rapid fabrication of custom patient biopsy guides. J Appl Clin Med Phys 2009;10:2897.
Singare S, Dichen L, Bingheng L, Yanpu L, Zhenyu G, Yaxiong L, et al.
Design and fabrication of custom mandible titanium tray based on rapid prototyping. Med Eng Phys 2004;26:671-6.
Jenkins C, Xing L. SU-E-J-49: Design and fabrication of custom 3D printed phantoms for radiation therapy research and quality assurance. Med Phys 2015;42:3274-5.
Lovato C, Milanese C, Piscitelli F, Zancanaro C, Giachetti A. Health-Related Shape Analysis of 3D Body Scanner Data. Proceedings of the 2nd
International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 25-26 October, 2011; 2011.
Omrcen D, Damir O, Ales J. Difference in Shape and Dimensions between Adult and Children Feet Based on 40.000 3D Scans. Proceedings of the 4th
International Conference on 3D Body Scanning Technologies, Long Beach CA, USA, 19-20 November, 2013; 2013.
Barnes R, Richard B. 3D Measurement of Children - Shape GB - The UK National Childrenswear Survey. Proceedings of the 2nd
International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 25-26 October, 2011; 2011.
Thali MJ, Braun M, Dirnhofer R. Optical 3D surface digitizing in forensic medicine: 3D documentation of skin and bone injuries. Forensic Sci Int 2003;137:203-8.
Banno A, Masuda T, Ikeuchi K. Three dimensional visualization and comparison of impressions on fired bullets. Forensic Sci Int 2004;140:233-40.
Thali MJ, Marcel B, Joachim W, Peter V, Richard D. 3D surface and body documentation in forensic medicine: 3-D/CAD photogrammetry merged with 3D radiological scanning. J Forensic Sci 2003;48:2003118.
Mahmoud A, Bennett M. Introducing 3-dimensional printing of a human anatomic pathology specimen: Potential benefits for undergraduate and postgraduate education and anatomic pathology practice. Arch Pathol Lab Med 2015;139:1048-51.
Prakash S, Venkataraman S, Slanetz PJ, Dialani V, Fein-Zachary V, et al.
Improving Patient Care by Incorporation of Multidisciplinary Breast Radiology-Pathology Correlation Conference. Can Assoc Radiol J 2016; 67:122-129.
Kadambi A, Taamazyan V, Shi B, Raskar R. Polarized 3D: High-Quality Depth Sensing with Polarization Cues. Proceedings of the IEEE International Conference on Computer Vision; 2015. p. 3370-8.
Hanna M, Nine J, Prajapati S, Ishtiaque A, Wiley C, Pantanowitz L. Multiple use cases for Microsoft HoloLens in Pathology. Mod Pathol 2017;30 Suppl 2:396A-7A.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4]
|This article has been cited by|
||Capturing patient anatomy for designing and manufacturing personalized prostheses
| ||Naomi C Paxton, Renee C Nightingale, Maria A Woodruff |
| ||Current Opinion in Biotechnology. 2022; 73: 282 |
|[Pubmed] | [DOI]|
||Differentiation of benign and malignant regions in paraffin embedded tissue blocks of pulmonary adenocarcinoma using micro CT scanning of paraffin tissue blocks: a pilot study for method validation
| ||Ayten Kayi Cangir, Serpil Dizbay Sak, Gökalp Günes, Kaan Orhan |
| ||Surgery Today. 2021; 51(10): 1594 |
|[Pubmed] | [DOI]|
||Time-of-flight sensor for getting shape model of automobiles toward digital 3D imaging approach of autonomous driving
| ||Ilpo Niskanen, Matti Immonen, Lauri Hallman, Genki Yamamuchi, Martti Mikkonen, Takeshi Hashimoto, Yasushi Nitta, Pekka Keränen, Juha Kostamovaara, Rauno Heikkilä |
| ||Automation in Construction. 2021; 121: 103429 |
|[Pubmed] | [DOI]|
||3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies
| ||Zhijie Zhu, Daniel Wai Hou Ng, Hyun Soo Park, Michael C. McAlpine |
| ||Nature Reviews Materials. 2021; 6(1): 27 |
|[Pubmed] | [DOI]|
||Medical image interpretation training with a low-cost eye tracking and feedback system: A preliminary study
| ||Mathieson Tan Zui Quen, J. Mountstephens, Yong Guang Teh, J. Teo |
| ||Healthcare Technology Letters. 2021; 8(4): 97 |
|[Pubmed] | [DOI]|
||Kidney Allograft Fibrosis: Diagnostic and Therapeutic Strategies
| ||Turgay Saritas, Rafael Kramann |
| ||Transplantation. 2021; 105(10): e114 |
|[Pubmed] | [DOI]|
||Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples
| ||Alton B Farris, Juan Vizcarra, Mohamed Amgad, Lee A D Cooper, David Gutman, Julien Hogan |
| ||Histopathology. 2021; 78(6): 791 |
|[Pubmed] | [DOI]|
||3D Mueller Matrix Reconstruction of the Optical Anisotropy Parameters of Myocardial Histopathology Tissue Samples
| ||Benjamin T. Hogan, Volodimyr A. Ushenko, Anastasia-Vira Syvokorovskaya, Alexander V. Dubolazov, Oleg Ya. Vanchulyak, Alexander G. Ushenko, Yuriy A. Ushenko, Mykhailo P. Gorsky, Yuriy Tomka, Sergey L. Kuznetsov, Alexander Bykov, Igor Meglinski |
| ||Frontiers in Physics. 2021; 9 |
|[Pubmed] | [DOI]|
||First Report and 3D Reconstruction of a Presumptive Microscopic Liver Lipoma in a Black Barbel (Barbus balcanicus) from the River Bregalnica in the Republic of North Macedonia
| ||Katerina Rebok, Maja Jordanova, Júlia Azevedo, Eduardo Rocha |
| ||Applied Sciences. 2021; 11(18): 8392 |
|[Pubmed] | [DOI]|
||The Role of 3D Imaging in the Practice of Medicine and Medical Education
| ||Chaya Prasad, Sharon Lee, Jenny Vang |
| ||Medical Journal of Southern California Clinicians. 2020; : 12 |
|[Pubmed] | [DOI]|
||Banff Digital Pathology Working Group: Going digital in transplant pathology
| ||Alton B. Farris, Ishita Moghe, Simon Wu, Julien Hogan, Lynn D. Cornell, Mariam P. Alexander, Jesper Kers, Anthony J. Demetris, Richard M. Levenson, John Tomaszewski, Laura Barisoni, Yukako Yagi, Kim Solez |
| ||American Journal of Transplantation. 2020; 20(9): 2392 |
|[Pubmed] | [DOI]|
||3D histopathology of human tumours by fast clearing and ultramicroscopy
| ||Inna Sabdyusheva Litschauer, Klaus Becker, Saiedeh Saghafi, Simone Ballke, Christine Bollwein, Meraaj Foroughipour, Julia Gaugeler, Massih Foroughipour, Viktória Schavelová, Viktória László, Balazs Döme, Christine Brostjan, Wilko Weichert, Hans-Ulrich Dodt |
| ||Scientific Reports. 2020; 10(1) |
|[Pubmed] | [DOI]|
||Detection and assessment of capsular invasion, vascular invasion and lymph node metastasis volume in thyroid carcinoma using microCT scanning of paraffin tissue blocks (3D whole block imaging): a proof of concept
| ||Bin Xu, Alexei Teplov, Kareem Ibrahim, Takashi Inoue, Ben Stueben, Nora Katabi, Meera Hameed, Yukako Yagi, Ronald Ghossein |
| ||Modern Pathology. 2020; 33(12): 2449 |
|[Pubmed] | [DOI]|