accuracy over the most accurate baseline state-of-the-art Big-to-Small (BTS) approach. All experiments are conducted on a recently released C3VD dataset, and thus, we provide a first benchmark of state-of-the-art methods on this dataset.

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas and generate realistic synthetic shapes. We additionally extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability and preserve more details in the generated shapes. Experimental results using liver and left-ventricular models demonstrate the approach's applicability to computational medicine, highlighting its suitability for ISCTs through a comparative analysis.

Ultrasound imaging faces particular challenges with high inter-operator variability and manual inspection of abnormalities. Deep learning segmentation methods are progressing rapidly to address these clinical challenges with improved automatic segmentation performance combining convolutional neural network (CNN) and Transformer approaches. However, challenges still remain with poor performance in boundary areas due to high speckle noise and training models with limited training data. This paper demonstrates a comparison of EfficientNet B2, EfficientNet B7 and PVT-v2-B5 pre-trained encoder backbones in a U-Net architecture. A noticeable improvement for all three pre-trained backbones is shown particularly in smaller Breast Ultrasound datasets with limited training data (EfficientNet B2: 4.5%, EfficientNet B7: 3.9%, PVT-v2-B5: 2.4%). However, the improvement is marginal (less than 1%) in the larger Nerve Ultrasound dataset. In addition, we noticed that the performance across all backbones is better in segmenting regular regions of interest (e.g. benign breast lesions), over irregular shapes (e.g. malignant breast lesions). The code used for this study is available at: https://github.com/aimsgroup-Leeds/IEEECBMS2024_US_Seg

We propose a novel method for generating high-resolution videos of talking-heads from speech audio and a single 'identity' image. Our method is based on a convolutional neural network model that incorporates a pre-trained StyleGAN generator. We model each frame as a point in the latent space of StyleGAN so that a video corresponds to a trajectory through the latent space. Training the network is in two stages. The first stage is to model trajectories in the latent space conditioned on speech utterances. To do this, we use an existing encoder to invert the generator, mapping from each video frame into the latent space. We train a recurrent neural network to map from speech utterances to displacements in the latent space of the image generator. These displacements are relative to the back-projection into the latent space of an identity image chosen from the individuals depicted in the training dataset. In the second stage, we improve the visual quality of the generated videos by tuning the image generator on a single image or a short video of any chosen identity. We evaluate our model on standard measures (PSNR, SSIM, FID and LMD) and show that it significantly outperforms recent state-of-the-art methods on one of two commonly used datasets and gives comparable performance on the other. Finally, we report on ablation experiments that validate the components of the model. The code and videos from experiments can be found at https://mohammedalghamdi.github.io/talking-heads-acm-mm/

MOTIVATION: Functional genomics data provides a rich source of information that can be used in the annotation of the thousands of genes of unknown function found in most sequenced genomes. However, previous gene function prediction programs are mostly produced for relatively well-annotated organisms that often have a large amount of functional genomics data. Here, we present a novel method for predicting gene function that uses clustering of genes by semantic similarity, a naïve Bayes classifier and 'enrichment analysis' to predict gene function for a genome that is less well annotated but does has a severe effect on human health, that of the malaria parasite Plasmodium falciparum. RESULTS: Predictions for the molecular function, biological process and cellular component of P.falciparum genes were created from eight different datasets with a combined prediction also being produced. The high-confidence predictions produced by the combined prediction were compared to those produced by a simple K-nearest neighbour classifier approach and were shown to improve accuracy and coverage. Finally, two case studies are described, which investigate two biological processes in more detail, that of translation initiation and invasion of the host cell. AVAILABILITY: Predictions produced are available at http://www.bioinformatics.leeds.ac.uk/∼bio5pmrt/PAGODA.

MOTIVATION: To predict which of the vast number of human single nucleotide polymorphisms (SNPs) are deleterious to gene function or likely to be disease associated is an important problem, and many methods have been reported in the literature. All methods require data sets of mutations classified as 'deleterious' or 'neutral' for training and/or validation. While different workers have used different data sets there has been no study of which is best. Here, the three most commonly used data sets are analysed. We examine their contents and relate this to classifiers, with the aims of revealing the strengths and pitfalls of each data set, and recommending a best approach for future studies. RESULTS: The data sets examined are shown to be substantially different in content, particularly with regard to amino acid substitutions, reflecting the different ways in which they are derived. This leads to differences in classifiers and reveals some serious pitfalls of some data sets, making them less than ideal for non-synonymous SNP prediction. AVAILABILITY: Software is available on request from the authors.

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BACKGROUND: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. RESULTS: Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables. CONCLUSION: The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

In this paper we present a system that combines the benefits of 3D deformable models and level set methods for medical volume segmentation. Our 3D deformable model is a very computationally efficient method for segmenting medical volumes, however it is not currently able to segment features, such as renal arteries, that are small relative to the imaging slice thickness used. Level Set methods are an alternative approach to deformable models that re-pose the volume segmentation problem as the calculation of the steady state of an initial value Partial Differential Equation (PDE) system on a regular rectilinear or cubic mesh. The segmentation obtained is parameterised by the zero value level set of this mesh (analogous to an iso-surface). These methods are very computationally expensive, but have the advantage of being able to segment relatively small features such as renal arteries. The problem domain explored in this paper is the segmentation of arterial structures. The results of these segmentations are to be used in the assessment of patient suitability for minimally invasive (keyhole) surgical procedures in patients with abnormal aortic aneurysms. An abdominal aortic aneurysm (AAA) is a dilation of the abdominal aorta. AAAs usually increase in size with time, and if left untreated eventually rupture causing catastrophic haemorrhage. An AAA may be treated by conventional surgical methods, but increasingly minimally invasive techniques, where a stent graft is placed in the lumen, are being used. Patient suitability is assessed using CT data and a calibrated projection angiogram - only about 10% of patients are suitable for the keyhole repair. Once a candidate has been assessed as suitable, measurements are made from the same images to determine the key dimensions of the required stent. Our overall aim is to automate both of these stages of image analysis, ensuring that the full 3D nature of the CT is used. In this paper we describe a segmentation aproach that combines the benefits of a 3D deformable model

Rule-based systems employed to model complex object behaviours, do not necessarily provide a realistic portrayal of true behaviour. To capture the real characteristics in a specific environment, a better model may be learnt from observation. This paper presents a novel approach to learning longterm spatio-temporal patterns of objects in image sequences, using a neural network paradigm to predict future behaviour. The results demonstrate the application of our approach to the problem of predicting animal behaviour in response to a predator. 1 Introduction The recognition of spatio-temporal patterns within a scene is an important facet of computer vision research. Future behaviour of an object, in terms of its motion and appearance, can be implied through a learned model of previous behaviour. Short-term predicitions of likely object motion and deformation over one time-step allow objects to be tracked robustly through a scene. This has been achieved successfully using a Kalman...

Deformable models are a powerful and popular tool for image segmentation, but in 3D imaging applications the high computational cost of fitting such models can be a problem. A further drawback is the need to select the initial size and position of a model in such a way that it is close to the desired solution. This task may require particular expertise on the part of the operator, and, furthermore, may be difficult to accomplish in three dimensions without the use of sophisticated visualisation techniques. This article describes a 3D deformable model that uses an adaptive mesh to increase computational efficiency and accuracy. The model employs a distance transform in order to overcome some of the problems caused by inaccurate initialisation. The performance of the model is illustrated by its application to the task of segmentation of 3D MR images of the human head.