Healed or non-healed? Computed tomography (CT) visualisation of morphology of bite trace ichnotaxa on a dinosaur bone

Bite traces on fossilised bones can provide important information on predator-prey relations and interactions in ancient environments. In 2009, two new ichnotaxa, Linichnus serratus and Knethichnus parallelum, were introduced to develop the application of bite traces as an ichnological tool. Ichnotaxa defined by theropod bite traces can provide useful information for understanding feeding behaviour. However, objective interpretation of possible bite traces can be difficult using traditional visual inspection. In this study, the bite traces on a fossilised dinosaur bone were comprehensively examined by correlating traditional naked-eye in spection with computed tomography (CT) imaging, used to visualise the internal morphology of the bite traces and in particular, to clarify the appearance of one possibly healed bite trace. A forensic pathologist visually examined the bone with the aid of stereomicroscopy and a radiologist analysed the CT scans. Sixteen different scanner settings were used to optimise the CT parameters and avoid signal at tenuation, in the form of hypointense artefacts in the central trabeculated part of the bone fragment. The use of CT scanning provided information on internal morphology from the vicinity of the bite trace, including hyperdense zones, not identified using visual inspection alone. By applying the extended CT scale, the dense and radiopaque cortical bone layer could be clearly identified and applied as a pathomorphological marker to correctly distinguish non-healed from healed wounds. In conclusion, the authors demonstrate that external visual examination of trace fossils by ichnologists in combination with interior examination using CT imaging can be applied to characterise ichnotaxa defined by bite traces and potentially provide clues on ancient feeding behaviour

Three-Dimensional Segmentation of the Tumor in Computed Tomographic Images of Neuroblastoma

Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to therapy and in the planning of delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for the tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children's Hospital. False-negative error rates of less than 12% were obtained in eight of the 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist