Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients

Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. We applied a modified light-weight GAN (FastGAN) algorithm to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients. Investigating three anatomical levels (cerebellum, CER; basal ganglia, BG; cortex, COR), 551 normal (248 CER, 174 BG, 129 COR) and 387 pathological brain SPECTs using N-isopropyl p-I-123-iodoamphetamine (I-123-IMP) were included. For the latter scans, cerebral ischemic disease comprised 291 uni- (66 CER, 116 BG, 109 COR) and 96 bilateral defect patterns (44 BG, 52 COR). Our model was trained using a three-compartment anatomical input (dataset 'A'; including CER, BG, and COR), while for dataset 'B', only one anatomical region (COR) was included. Quantitative analyses provided mean counts (MC) and left/right (LR) hemisphere ratios, which were then compared to quantification from real images. For MC, 'B' was significantly different for normal and bilateral defect patterns (P = 0.08) reached significance relative to images of real patients. With a minimum of only three anatomical compartments serving as stimuli, created cerebral SPECTs are indistinguishable to images from real patients. The applied FastGAN algorithm may allow to provide sufficient scan numbers in various clinical scenarios, e.g., for "data-hungry" deep learning technologies or in the context of orphan diseases

Learning to synthesise the ageing brain without longitudinal data

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing

Deep generative models for medical image synthesis and strategies to utilise them

Medical imaging has revolutionised the diagnosis and treatments of diseases since the first medical image was taken using X-rays in 1895. As medical imaging became an essential tool in a modern healthcare system, more medical imaging techniques have been invented, such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Computed Tomography (CT), Ultrasound, etc. With the advance of medical imaging techniques, the demand for processing and analysing these complex medical images is increasing rapidly. Efforts have been put on developing approaches that can automatically analyse medical images. With the recent success of deep learning (DL) in computer vision, researchers have applied and proposed many DL-based methods in the field of medical image analysis. However, one problem with data-driven DL-based methods is the lack of data. Unlike natural images, medical images are more expensive to acquire and label. One way to alleviate the lack of medical data is medical image synthesis. In this thesis, I first start with pseudo healthy synthesis, which is to create a ‘healthy’ looking medical image from a pathological one. The synthesised pseudo healthy images can be used for the detection of pathology, segmentation, etc. Several challenges exist with this task. The first challenge is the lack of ground-truth data, as a subject cannot be healthy and diseased at the same time. The second challenge is how to evaluate the generated images. In this thesis, I propose a deep learning method to learn to generate pseudo healthy images with adversarial and cycle consistency losses to overcome the lack of ground-truth data. I also propose several metrics to evaluate the quality of synthetic ‘healthy’ images. Pseudo healthy synthesis can be viewed as transforming images between discrete domains, e.g. from pathological domain to healthy domain. However, there are some changes in medical data that are continuous, e.g. brain ageing progression. Brain changes as age increases. With the ageing global population, research on brain ageing has attracted increasing attention. In this thesis, I propose a deep learning method that can simulate such brain ageing progression. Specifically, longitudinal brain data are not easy to acquire; if some exist, they only cover several years. Thus, the proposed method focuses on learning subject-specific brain ageing progression without training on longitudinal data. As there are other factors, such as neurodegenerative diseases, that can affect brain ageing, the proposed model also considers health status, i.e. the existence of Alzheimer’s Disease (AD). Furthermore, to evaluate the quality of synthetic aged images, I define several metrics and conducted a series of experiments. Suppose we have a pre-trained deep generative model and a downstream tasks model, say a classifier. One question is how to make the best of the generative model to improve the performance of the classifier. In this thesis, I propose a simple procedure that can discover the ‘weakness’ of the classifier and guide the generator to synthesise counterfactuals (synthetic data) that are hard for the classifier. The proposed procedure constructs an adversarial game between generative factors of the generator and the classifier. We demonstrate the effectiveness of this proposed procedure through a series of experiments. Furthermore, we consider the application of generative models in a continual learning context and investigate the usefulness of them to alleviate spurious correlation. This thesis creates new avenues for further research in the area of medical image synthesis and how to utilise the medical generative models, which we believe could be important for future studies in medical image analysis with deep learning