Automatic hard tissue segmentation

Computed tomography (CT) scanning is a standard way of visualizing hard tissues in living organisms. However, tridimensional reconstruction of CT images requires segmenting the structure at hand, which is time-consuming at best (i.e., manual segmentation) and imprecise at worst (i.e., automatic segmentation), especially in multipart segmentation. To circumvent this issue, Didziokas et al. (2024) have developed an open-access, user-friendly, automated segmentation tool for hard tissues, focusing especially on skull bones: boundary-preserving threshold iteration (BounTI). As its name suggests, BounTI’s operators select the structure of interest based on voxel intensity, which is the only input parameter it needs from the user. This procedure yields good results for bone segmentation, given that osseous tissue usually presents a distinct voxel intensity when compared with its surroundings. An appropriate initial threshold of voxel intensity is one that does not cause separate elements to be joint in the seed (that is, the first recognition of tissue by the algorithm), and which does not cause erroneous separations of single elements (e.g., the parietal bone) in the final stage.

BounTI was tested on skull CT images of various species, including amphibians, reptiles, and mammals. The quality of the assessment’s results demonstrates BounTI’s versatility and effectiveness. However, its performance is bound by the quality of the image; lower resolutions yield worse results. To mend any errors that might arise, BounTI does include options for manual intervention. Lastly, the authors emphasize the tool’s accessibility, human and machine-wise. BounTI can be implemented in a plethora of ways, holding great potential to improve efficiency and accuracy in anatomical studies and clinical applications involving hard tissue segmentation.

Tim Schuurman

Inside reptile skulls

Research on brain evolution is becoming more and more interested in reptiles. The ecological diversity and complex sensory systems of snakes, for example, make them great models for investigating the link between structure and function.

Segall et al. tested whether they could link the endocranial morphology of snakes to ecological factors affecting sensory processing. They used a phylogenetically diverse sample of 36 snakes species with different aquatic habits to ensure the results reflected specific sensory adaptations to shared ecological pressures. They considered the species’ differences in foraging habitat (land or water) and activity period (diurnal, nocturnal, cathemeral), among other sensory-ecology factors. The snakes’ endocrania were reconstructed from micro-CT data and analyzed through 3D geometric morphometrics. The results showed that size drove shape variation across the first component. It distinguished between the long and narrow endocrania of large-headed snakes and the more globular ones of small-headed snakes. Shape variation along the second component reflected phylogenetic differences, mainly between the smaller snake species. Regarding the ecological traits, the authors observed that the activity period was associated with shape variation in the optic and olfactory tracts, and the foraging habitat was associated with shape changes in the cerebellum. This study shows that endocranial shape can supply valuable information in the studies of sensory adaptations of snakes; however, it cannot infer snake ecology without phylogenetic information.

Studies like this, linking structure and function, especially if using brain data, can benefit from reference brain models and atlases to identify the anatomical areas.

In the same issue of Brain Structure and Function, Hoops et al. have published an atlas of a lizard brain based on a consensus model generated from MRIs from 13 male tawny dragons (Ctenophorus decresii). Using the average of several individuals instead of a single brain increases the contrast and resolution of the atlas and helps to identify the boundaries between adjacent structures. Apart from the olfactory bulbs, the 3D atlas includes 224 brain structures segmented on the left hemisphere. This atlas is available as supplementary material and through the Open Science Framework. In the future, atlases should make use of multimodal imaging and include histological and connectivity information.

Sofia Pedro