Statistical modelling for hard and soft tissue of the human head

Abstract

First proposed over 20 years ago, 3D morphable models (3DMMs) provide an insight into the statistical shape variations of a given shape class. Craniofacial anomalies represent a diverse class of conditions in which one or more of the sutures in the infant cranium fuses prematurely, causing a deviation in the growth pattern of the skull from that of the unaffected population. 3DMMs present an opportunity to analyse these shape deviations from a statistical perspective and can be used for the development of diagnostic and surgical planning tools. For this to be achieved, however, several boundary limitations must be overcome. Constructing statistical shape models for a population affected by craniosynostosis is by no means a straightforward task. As is suggested by the description “craniofacial anomalies”, instances of craniosynostosis are rare. Consequently, it can be difficult to collect sufficient quantities of data to accurately model the shape variations of the affected population. While 3D facial and cranial datasets are becoming increasingly available, these datasets tend to be comprised of adolescent and adult samples and few contain instances of infants and young children. Even when adequate 3D data has been collected, annotation is expensive and time consuming. Finally, while mesh autoencoders have proven effective for accurate mesh reconstruction and novel shape generation, there are many advances yet to be made in this domain. This thesis aims to make progress addressing the aforementioned issues in a number of ways. To address some of the existing limitations surrounding sparse 3D annotation, we present two methods for landmark localisation in 3D point clouds: i) point cloud segmentation and offset vector prediction are combined to localise landmarks within the input cloud and ii) the concept of convolutional pose machines is extended to predict landmark heatmaps for 3D facial point clouds. Both methods are shown to be robust to a range of input point cloud sizes and can be readily applied for landmark localisation in any shape class given appropriate training data. To aid in surgical reconstruction procedures for ear microtia we propose an approach to infer the underlying ear cartilage shape from that of the external ear soft tissue. We achieve this by generalising the convolutional mesh autoencoder framework such that the shape class and mesh topology of the encoder and decoder need not be identical. We further introduce an intersection loss to enforce the spatial relationship between the outer ear and underlying cartilage and improve the robustness of predicted results. As normal models of the paediatric population are limited, we collect a dataset of CT scans for infants aged between 0 and 48 months. From this dataset we construct 3DMMs of the face, head, skull, and mandible. By collating data from multiple sources, we build facial and head models to aid in the diagnosis of craniofacial anomalies related to mutations in the FGFR gene. High diagnostic accuracy is achieved, and experiments demonstrate that the facial features contain important information for the correct diagnosis of syndromic craniosynostosis. Finally, neural approaches to the construction of 3DMMs can result in a loss of interpretability and regularisation in the model latent space. To address this, we propose PCA retargeting, a method for expressing linear PCA models as convolutional mesh autoencoders and thereby retaining the gaussianity of the latent space.