Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations.Comment: 8 pages, 4 figures, MICCA

Development of a statistical shape and appearance model of the skull from a South African population

Statistical shape models (SSMs) and statistical appearance models (SAMs) have been applied in medical analysis such as in surgical planning, finite element analysis, model-based segmentation, and in the fields of anthropometry and forensics. Similar applications can make use of SSMs and SAMs of the skull. A combination of the SSM and SAM of the skull can also be used in model-based segmentation. This document presents the development of a SSM and a SAM of the human skull from a South African population, using the Scalismo software package. The SSM development pipeline was composed of three steps: 1) Image data segmentation and processing; 2) Development of a free-form deformation (FFD) model for establishing correspondence across the training dataset; and 3) Development and validation of a SSM from the corresponding dataset. The SSM was validated using the leave one-out cross-validation method. The first eight principal components of the SSM represented 92.13% of the variation in the model. The generality of the model in terms of the Hausdorff distance between a new shape generated by the SSM and instances of the SSM had a steady state value of 1.48mm. The specificity of the model (in terms of Hausdorff distance) had a steady state value of 2.04mm. The SAM development pipeline involved four steps: 1) Volumetric mesh generation of the reference mesh to be used in establishing volumetric correspondence; 2) Sampling of intensity values from original computed tomography (CT) images using the in-correspondence volumetric meshes; and 3) Development of a SAM from the in-correspondence intensity values. A complete validation of the SAM was not possible due to limitations of the Scalismo software. As a result, only the shapes of the incomplete skulls were reconstructed and thereby validated. The amount of missing detail, as represented by absent landmarks, affected the registration results. Complete validation of the SAM is recommended as future work, via the use of a combined shape and intensity model (SSIM)

Human identification: an investigation of 3D models of paranasal sinuses to establish a biological profile on a modern UK population

Forensic anthropology traditionally aims to assist law enforcement with human identification by physically examining skeletal remains and assigning a biological profile using various metric and visual methods. These methods are crucial when a body undergoes extreme damage and standard approaches for positive identification are not possible. However, the traditional methods employed by forensic anthropologists were primarily developed from North American reference populations and have demonstrated varying accuracy rates when assigning age, sex, and ancestry to individuals outside of the reference collection. Medical imaging is a valuable source for facilitating empirical research and an accessible gateway for developing novel forensic anthropological methods for analysis including 3D modelling. This is especially critical for the United Kingdom (UK) where biological profiling methods developed from modern UK populations do not currently exist. Researchers have quantified the variability of the paranasal sinuses between individuals and have begun to explore their ability to provide biological information. However, the published literature that addresses these structures in a forensic context presents extremely varied insights and to date there has been no standardisation. This thesis presents research that addresses this gap and introduces a new approach for human identification using 3D models of the paranasal sinuses. The models were produced from a database of modern CT scans provided by University College London Hospital (UCLH), London, UK. Linear measurements and elliptic Fourier coefficients taken from 1,500 three-dimensional models across six ethnic groups assessed by one-way ANOVA and discriminant function analysis showed a range of classification rates with certain rates reaching 75-85.7% (p<0.05) in correctly classifying age and sex according to size and shape. The findings offer insights into the potential for employing CT scans to develop identification methods within the UK and establishes a foundation for using the paranasal sinuses as an attribute for establishing identification of unknown human remains in future crime reconstructions