Tag Archives: Digital anatomy

Digital Endocasts

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Close-range Photogrammetry or Surface Scanner?

postRecently, Evin et al. 2016 have published a study comparing the accuracy of the three-dimensional reconstruction of five wolf crania using both photogrammetry and high-resolution surface scanner. For the photogrammetric images acquisition, they used an 8-megapixel (DSLR) Canon EOS 30D camera, mounted with a Canon EF 24–105mmf/4 L IS USMlens. The scanner-based 3D models were created using a Breuckmann StereoScan structured light scanner (http://www.breuckmann.com). The resulting 3D models were compared first through visual observation, and second with the computation of a mesh-to-mesh deviation map. The pairs of models were spatially aligned (using a least-square optimisation best-fit criterion), followed by a 3D landmark-based geometric morphometric approach using corresponding analyses. The results show that photogrammetric 3D models are as accurate in terms of coloration, texture, and geometry, as the highest-end surface scanners. Minimal differences between photogrammetric 3D models and surface scanner-based models have been only identified on intricate topological regions, such the tooth row. Photogrammetry is becoming a common tool in archaeological and anthropological research. The major advantage of this technique is the speed and ease of image acquisition and reconstruction. Photogrammetry is an equally good alternative and less expensive than other more common techniques, such as structured light or surface scanners. In terms of archaeological samples conservation, photogrammetry could be in the future an excellent alternative to provide accurate replica models that can be widely accessible for research, without affecting the original collections.

Gizéh Rangel de Lázaro

Structural MRI artifacts

Magnetic resonance imaging (MRI) is a valuable and increasingly used method for studying brain anatomy as it allows large-scale, high-quality in vivo analyses. However, some artifacts might influence the digital results, and thus require cautious interpretation. In a recent review, these issues are addressed along with possible solutions. First, we need to keep in mind that the images acquired are not mere photographs of the brain, but reflect some biophysical properties of the tissues, by measuring the radio-frequency signals emitted by hydrogen atoms (present in water and fat) after being excited by magnetic waves. Thus, MRI is an indirect analysis of the brain anatomy and depends greatly on specific tissue properties. Second, researchers can choose from a variety of methods, depending on the aim of the survey. Macrostructure, i.e. the size and shape across voxels, can be studied through manual volumetry or automatic segmentation, voxel- or deformation-based morphometry, surface- based algorithms, or diffusion tractography. Microstructure, i.e. within-voxel contents, is usually analyzed through diffusion MRI, but also magnetization transfer imaging, or quantitative susceptibility mapping.

When making inferences on the biological significance of the outputs, the researcher must account for the possible digital artifacts. These can occur both during image acquisition and processing and can be subject-related and methodological-related. A common problem is subject motion, which might contaminate or influence the results, as the amount of motion varies with other factors influencing brain changes (age, sex, and disease status), or can even correlate with a specific effect being studied. For instance, motion induces gray matter reduction, which might be perceived as brain atrophy. Subject motion is unavoidable, but its influence can be reduced by using a motion detector during acquisition, or by estimating the amount of motion allowing statistical adjustments, also useful to  detect outliers. The difficulty in manipulating the magnetic and radio-frequency fields might also introduce deformation. The main magnetic field should be spatially uniform, but it is dispersed by brain tissue while concentrated by air. This can be partially compensated by applying additional fields. The radiofrequency field is not homogeneous, which affects MRI contrast and intensity. Combining multiple transmit coils might help reduce this caveat, although the contribution and sensitivity of each coil must be taken into account when processing the image.

A particular case that can affect estimates of cortical volume and thickness is the difficulty in discriminating the dura and gray matter due to the similar intensity and anatomical proximity. In this case,  MRI parameters can be manipulated in order to increase the contrast between these tissues, without reducing the contrast between gray and white matter. Individual variability in folding patterns is a further major issue in voxel-based morphometry studies because it complicates the mapping of correspondences between subjects. Registration might be enhanced by analyzing regions with larger variation to find possible anatomical alterations, aligning cortical folding patterns to locate corresponding areas, and mapping sulcal changes to improve sulci identification. Finally, researchers should continuously keep track of the constant advances and innovations in the field. The authors conclude acknowledging the importance of structural MRI when coupled with other biological information, like genetic expression (Allen Brain Atlas), cytoarchitecture (JuBrain), and cognitive associations (Neurosynth).

Sofia Pedro

Open-access 3D morphological datasets

morphosourceMorphoSource, is a dataset project for storing, collaborative sharing, and distribution of microCT scans, 3D surface rendering, and 2D digital imaging. Its main goal is to provide rapid access to as many researchers as possible, large numbers of raw microCT data, and surface meshes representing vouchered specimens. The site is active since April 2013 and now hosts almost 5000 files, including ‘raw’ microCT volumetric data, mesh files  from laser scans, and 2D digital photographs (file formats include tiff, dicom, stanford ply, and stl). MorphoSource, allow to download a world-wide repository-vouchered digitalized specimens from 48 institutions (American Museum of Natural History; Muséum National d’Histoire Naturelle; Natural History Museum Vienna, see more in Institutions). Currently the amount of information related with genus Homo is limited to Homo sapiens; however the site dataset is growing rapidly, and in the future it will be an interesting source of data for Paleoanthropologists too.

Gizéh Rangel de Lázaro

Digital reconstruction

piece“Replacement of Neanderthals by Modern Humans” (RNMH) is a project aimed at investigating possible cognitive and behavioural  differences between these two human groups. I belong to the team coordinated by Naomichi Ogihara at the Keio University, and we have now published a review article  about digital reconstruction of fossil crania and analysis of their brain morphology. As engineers, we are trying to reconstruct the brain anatomy of Neanderthals and early modern humans according to numerical approaches and mathematical models. Soft tissues do not fossilize, so we are trying to provide a spatial estimation of the brain anatomical organization. As a first step, restoration of the original cranial morphology is necessary, because fossil remains are often fractured, fragmented, and deformed because of the taphonomic and diagenetic processes. Digital tools and virtual simulation procedures are used to achieve a more precise and objective morphological reconstruction. Mathematical approaches are applied in such computational techniques. For example, cranial fragments are assembled based on smoothness (minimizing fitting error) of their joints. The deformation is corrected by affine transformation or thin-plate spline (TPS) function based on bilateral symmetry. Missing parts are interpolated by several mathematical approaches. This new paper reviews the current status of methods in computed anatomy, and it presents an overview on digital reconstruction of fossil crania, aimed at supplying computed methods to estimate their brain morphology.

Hideki Amano

Mapping the cranial vault

Thickness and density

More on vault thickness. As we commented in the previous post, the cranial vault can be divided in three layers: external table, diploe and internal table. Many previously studies provided information about skull thickness and density, but generally were based on a scarce number of measurements and with limited small sample size. Digital anatomy allows to go beyond many limits and constraints when working on this topic. Arne Voie and coauthors published a study based on parametric mapping and quantitative analysis of the human cranium. This team from San Diego, California, working mainly on neuroscience and radiological investigations, described how the thickness and density of the human skull changes depending on the anatomical regions (frontal, temporal, parietal and occipital bones). Measurements were computed on 51 dried crania of modern humans (males and females, ranging from 53 to 97 years old) and were analyzed by using 2000 points positioned on the three layers. Thickness and density distribution were calculated by using an algorithm to detect dense point of both extra and intra cranial boundaries and the lower density values of the diploic layer. The density results were mapped parametrically on each cranium to display their thicker areas and their distribution. The analysis evidenced a marked variation among the specimens. The thicker regions of the skull, namely the parietal, occipital, and frontal bones, have a mean value of 10.14 mm. In almost half of the sample the denser areas are the coronal and sagittal sutures, especially in their meeting point, while in the rest of the sample the density varies widely. The study cannot evidence sexual differences in both thickness and density.

Gizéh Rangel de Lázaro



Radiopaedia, skulls and brains, bones and vessels, a very nice imaging source on Tumblr!