Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

Optimal Geodesic Active Contours: Application to Heart Segmentation

We develop a semiautomated segmentation method to assist in the analysis of functional pathologies of the left ventricle of the heart. The segmentation is performed using an optimal geodesic active contour with minimal structural knowledge to choose the most likely surfaces of the myocardium. The use of an optimal segmentation algorithm avoids the problems of contour leakage and false minima associated with variational active contour methods. The resulting surfaces may be analysed to obtain quantitative measures of the heart's function. We have applied the proposed segmentation method to multislice MRI data. The results demonstrate the reliability and efficiency of this scheme as well as its robustness to noise and background clutter

Sentence level relation extraction via relation embedding

Relation extraction is a task of information extraction that extracts semantic relations from text, which usually occur between two named entities. It is a crucial step for converting unstructured text into structured data that forms a knowledge base, so that it may be used to build systems with special purposes such as business decision making and legal case-based reasoning. Relation extraction in sentence-level is the most common type, because relationships can be usually discovered within single sentences. One obvious example is the relationship between the subject and the object. As it has been studied for years, there are various methods for relation extraction such as feature based methods, distant supervision and recurrent neural networks. However, the following problems have been found in these approaches. (i) These methods require large amounts of human labelled data to train the model in order to get high accuracy. (ii) These methods are hard to be applied in real applications, especially in specialised domains where experts are required for both labelling and validating the data. In this thesis, we address these problems in two aspects: academic research and application development. In terms of academic research, we propose models that can be trained with less amount of labelled training data. The first approach trains the relation feature embedding, then it uses the feature embeddings for obtaining relation embeddings. To minimise the effect of designing handcraft features, the second approach adopts RNNs to automatically learn features from the text. In these methods, relation embeddings are reduced to a smaller vector space, and the relations with similar meanings form clusters. Therefore, the model can be trained with a smaller number of labelled data. The last approach adopts seq2seq regularisation, which can improve the accuracy of the relation extraction models. In terms of application development, we construct a prototype web service for searching semantic triples using relations extracted by third-party extraction tools. In the last chapter, we run all our proposed models on real-world legal documents. We also build a web application for extracting relations in legal text based on the trained models, which can help lawyers investigate the key information in legal cases more quickly. We believe that the idea of relation embeddings can be applied in domains that require relation extraction but with limited labelled data

High-energy ultraviolet dispersive-wave emission in compact hollow capillary systems

We demonstrate high-energy resonant dispersive-wave emission in the deep ultraviolet (218 to 375 nm) from optical solitons in short (15 to 34cm) hollow capillary fibres. This down-scaling in length compared to previous results in capillaries is achieved by using small core diameters (100 and 150 $\mu$m) and pumping with 6.3 fs pulses at 800 nm. We generate pulses with energies of 4 to 6 $\mu$J across the deep ultraviolet in a 100 $\mu$m capillary and up to 11 $\mu$J in a 150 $\mu$m capillary. From comparisons to simulations we estimate the ultraviolet pulse to be 2 to 2.5 fs in duration. We also numerically study the influence of pump duration on the bandwidth of the dispersive wave.Comment: 5 pages, 3 figure

Volume-aware design of composite molds

© 2019 Association for Computing Machinery. We propose a novel technique for the automatic design of molds to cast highly complex shapes. The technique generates composite, two-piece molds. Each mold piece is made up of a hard plastic shell and a flexible silicone part. Thanks to the thin, soft, and smartly shaped silicone part, which is kept in place by a hard plastic shell, we can cast objects of unprecedented complexity. An innovative algorithm based on a volumetric analysis defines the layout of the internal cuts in the silicone mold part. Our approach can robustly handle thin protruding features and intertwined topologies that have caused previous methods to fail. We compare our results with state of the art techniques, and we demonstrate the casting of shapes with extremely complex geometry

An automatic approach for classification and categorisation of lip morphological traits

Classification of facial traits (e.g., lip shape) is an important area of medical research, for example, in determining associations between lip traits and genetic variants which may lead to a cleft lip. In clinical situations, classification of facial traits is usually performed subjectively directly on the individual or recorded later from a three-dimensional image, which is time consuming and prone to operator errors. The present study proposes, for the first time, an automatic approach for the classification and categorisation of lip area traits. Our approach uses novel three-dimensional geometric features based on surface curvatures measured along geodesic paths between anthropometric landmarks. Different combinations of geodesic features are analysed and compared. The effect of automatically identified categories on the face is visualised using a partial least squares method. The method was applied to the classification and categorisation of six lip shape traits (philtrum, Cupid’s bow, lip contours, lip-chin, and lower lip tone) in a large sample of 4747 faces of normal British Western European descents. The proposed method demonstrates correct automatic classification rate of up to 90%

Development status of AEOLDOS - a deorbit module for small satellites

A prototype CubeSat module to deploy a gossamer aerobrake, using strain stored in tape-springs, at end-of-life is described. A novel hub geometry to reduce bending shock at end-of-deployment while simultaneously permitting radial, as opposed to tangential, deployment is proposed. The rpm of the hub is measured under various deployment conditions to verify that the system offers highly-repeatable performance, while high-speed photography is used to characterise the behaviour of the tape-spring during unspooling and contrast it to the behaviour of a traditional tangential-deployment system. Secondly the folding pattern of the membrane, which takes advantage of the symmetrical deployment offered by the petal hub, is developed and the unfolding mechanism is verified by numerical and experimental analysis. Finally, the release of the stored strain is considered and a novel burn-though device is designed and prototyped to meet this requirement