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- W2888672145 abstract "Computed tomography (CT) scans provide anthropologists with a resource to generate three-dimensional (3D) digital skeletal material to expand quantification methods and build more standardized reference collections. The ability to visualize and manipulate the bone and skin of the face simultaneously in a 3D digital environment introduces a new way for forensic facial approximation practitioners to access and study the face. Craniofacial relationships can be quantified with landmarks or with surface-processing software that can quantify the geometric properties of the entire 3D facial surface. This article describes tools for the generation of dense facial tissue depth maps (FTDMs) using deidentified head CT scans of modern Americans from the Cancer Imaging Archive public repository and the open-source program Meshlab. CT scans of 43 females and 63 males from the archive were segmented and converted to 3D skull and face models using Mimics and exported as stereolithography files. All subsequent processing steps were performed in Meshlab. Heads were transformed to a common orientation and coordinate system using the coordinates of nasion, left orbitale, and left and right porion. Dense FTDMs were generated on hollowed, cropped face shells using the Hausdorfff sampling filter. Two new point clouds consisting of the 3D coordinates for both skull and face were colorized on an RGB (red-green-blue) scale from 0.0 (red) to 40.0-mm (blue) depth values and exported as polygon (PLY) file format models with tissue depth values saved in the vertex quality field. FTDMs were also split into 1.0-mm increments to facilitate viewing of common depths across all faces. In total, 112 FTDMs were generated for 106 individuals. Minimum depth values ranged from 1.2 mm to 3.4 mm, indicating a common range of starting depths for most faces regardless of weight, as well as common locations for these values over the nasal bones, lateral orbital margins, and forehead superior to the supraorbital border. Maximum depths were found in the buccal region and neck, excluding the nose. Individuals with multiple scans at visibly diffferent weights presented the greatest diffferences within larger depth areas such as the cheeks and neck, with little to no diffference in the thinnest areas. A few individuals with minimum tissue depths at the lateral orbital margins and thicker tissues over the nasal bones (>3.0 mm) suggested the potential influence of nasal bone morphology on tissue depths. This study produced visual quantitative representations of the face and skull for forensic facial approximation research and practice that can be further analyzed or interacted with using free software. The presented tools can be applied to preexisting CT scans, traditional or cone beam, adult or subadult individuals, with or without landmarks, and regardless of head orientation, for forensic applications as well as for studies of facial variation and facial growth. In contrast with other facial mapping studies, this method produced both skull and face points based on replicable geometric relationships, producing multiple data outputs that are easily readable with software that is openly accessible." @default.
- W2888672145 created "2018-08-31" @default.
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- W2888672145 date "2018-01-01" @default.
- W2888672145 modified "2023-10-01" @default.
- W2888672145 title "Open-Source Tools for Dense Facial Tissue Depth Mapping of Computed Tomography Models" @default.
- W2888672145 cites W101787727 @default.
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- W2888672145 cites W1569530544 @default.
- W2888672145 cites W1595701647 @default.
- W2888672145 cites W1859058663 @default.
- W2888672145 cites W1860788083 @default.
- W2888672145 cites W1930432779 @default.
- W2888672145 cites W1948923808 @default.
- W2888672145 cites W1957046477 @default.
- W2888672145 cites W1968183843 @default.
- W2888672145 cites W1970641911 @default.
- W2888672145 cites W1972559119 @default.
- W2888672145 cites W1974963777 @default.
- W2888672145 cites W1975075234 @default.
- W2888672145 cites W1991185060 @default.
- W2888672145 cites W1999163003 @default.
- W2888672145 cites W2006502673 @default.
- W2888672145 cites W2006600146 @default.
- W2888672145 cites W2006776968 @default.
- W2888672145 cites W2019751592 @default.
- W2888672145 cites W2025948172 @default.
- W2888672145 cites W2026616100 @default.
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- W2888672145 cites W2027340909 @default.
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- W2888672145 cites W2046244693 @default.
- W2888672145 cites W2053799823 @default.
- W2888672145 cites W2055100978 @default.
- W2888672145 cites W2071287690 @default.
- W2888672145 cites W2073012520 @default.
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- W2888672145 cites W2075949143 @default.
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- W2888672145 doi "https://doi.org/10.13110/humanbiology.90.1.02" @default.
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