Generative adversarial networks in ophthalmology: what are these and how can they be used?

PURPOSE OF REVIEW: The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. RECENT FINDINGS: Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. SUMMARY: Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods

Multimodal and disentangled representation learning for medical image analysis

Automated medical image analysis is a growing research field with various applications in modern healthcare. Furthermore, a multitude of imaging techniques (or modalities) have been developed, such as Magnetic Resonance (MR) and Computed Tomography (CT), to attenuate different organ characteristics. Research on image analysis is predominately driven by deep learning methods due to their demonstrated performance. In this thesis, we argue that their success and generalisation relies on learning good latent representations. We propose methods for learning spatial representations that are suitable for medical image data, and can combine information coming from different modalities. Specifically, we aim to improve cardiac MR segmentation, a challenging task due to varied images and limited expert annotations, by considering complementary information present in (potentially unaligned) images of other modalities. In order to evaluate the benefit of multimodal learning, we initially consider a synthesis task on spatially aligned multimodal brain MR images. We propose a deep network of multiple encoders and decoders, which we demonstrate outperforms existing approaches. The encoders (one per input modality) map the multimodal images into modality invariant spatial feature maps. Common and unique information is combined into a fused representation, that is robust to missing modalities, and can be decoded into synthetic images of the target modalities. Different experimental settings demonstrate the benefit of multimodal over unimodal synthesis, although input and output image pairs are required for training. The need for paired images can be overcome with the cycle consistency principle, which we use in conjunction with adversarial training to transform images from one modality (e.g. MR) to images in another (e.g. CT). This is useful especially in cardiac datasets, where different spatial and temporal resolutions make image pairing difficult, if not impossible. Segmentation can also be considered as a form of image synthesis, if one modality consists of semantic maps. We consider the task of extracting segmentation masks for cardiac MR images, and aim to overcome the challenge of limited annotations, by taking into account unannanotated images which are commonly ignored. We achieve this by defining suitable latent spaces, which represent the underlying anatomies (spatial latent variable), as well as the imaging characteristics (non-spatial latent variable). Anatomical information is required for tasks such as segmentation and regression, whereas imaging information can capture variability in intensity characteristics for example due to different scanners. We propose two models that disentangle cardiac images at different levels: the first extracts the myocardium from the surrounding information, whereas the second fully separates the anatomical from the imaging characteristics. Experimental analysis confirms the utility of disentangled representations in semi-supervised segmentation, and in regression of cardiac indices, while maintaining robustness to intensity variations such as the ones induced by different modalities. Finally, our prior research is aggregated into one framework that encodes multimodal images into disentangled anatomical and imaging factors. Several challenges of multimodal cardiac imaging, such as input misalignments and the lack of expert annotations, are successfully handled in the shared anatomy space. Furthermore, we demonstrate that this approach can be used to combine complementary anatomical information for the purpose of multimodal segmentation. This can be achieved even when no annotations are provided for one of the modalities. This thesis creates new avenues for further research in the area of multimodal and disentangled learning with spatial representations, which we believe are key to more generalised deep learning solutions in healthcare

Outcomes after acute intracerebral haemorrhage

Primary Intracerebral haemorrhage is a severe form of stroke with poor prognosis attributed to haematoma characteristics. High blood pressure is present during the acute phase of intracerebral haemorrhage and associated with poor outcome in part through expansion of haematoma. Data from the ‘Efficacy of Nitric Oxide in Stroke trial’ (ENOS) was used to analyse the performance characteristics of qualitative and quantitative descriptors of intracerebral haematoma. The results showed that formal measurement of haemorrhage characteristics and visual estimates are reproducible. Intracerebral haemorrhage volumes measured using the modified ABC/2 formula were significantly lower compared to standard ABC/2 and computer assisted semi-automatic segmentation. In 629 patients with intracerebral haemorrhage presenting within 48 hours, the effect of blood pressure lowering with transdermal glyceryl trinitrate was assessed. Glyceryl trinitrate lowered blood pressure, was safe but did not improve functional outcome. In a small group of patients treated within 6 hours, glyceryl trinitrate improved functional outcome. Analysis of 246 patients with acute intracerebral haemorrhage from ENOS was undertaken to assess whether there were any differences in functional outcome among those who continued prior antihypertensive drugs during the immediate stroke period compared to those assigned to stop temporarily for 7 days. The results were neutral indicating that there was no benefit in those who continued treatment. Data of 1,011 patients with intracerebral haemorrhage in hyperacute trials from the VISTA collaboration showed differences in baseline characteristics and functional outcomes among patients from various ethnic backgrounds. A systematic review was updated to assess the effect of 26 randomised controlled trials that aimed to alter blood pressure within one week of acute stroke. The results showed that blood pressure reduction did not improve functional outcome irrespective of stroke type. When examined by time, treatment within 6 hours appeared to benefit but the number of patients were small and more studies are needed. The analysis also showed that continuing prestroke antihypertensive drugs in the immediate period after stroke did not benefit and might be harmful. In summary, this thesis provides new information on parameters used to estimate intracerebral haematoma, relationship between management of blood pressure and outcomes after haemorrhagic stroke. The work supports testing of whether very early blood pressure lowering after ictus is beneficial as is being undertaken in ongoing randomised controlled trials. Adjusting for ethnic differences may further identify patients in whom treatment may confer measurable advantage

Outcomes after acute intracerebral haemorrhage

Primary Intracerebral haemorrhage is a severe form of stroke with poor prognosis attributed to haematoma characteristics. High blood pressure is present during the acute phase of intracerebral haemorrhage and associated with poor outcome in part through expansion of haematoma. Data from the ‘Efficacy of Nitric Oxide in Stroke trial’ (ENOS) was used to analyse the performance characteristics of qualitative and quantitative descriptors of intracerebral haematoma. The results showed that formal measurement of haemorrhage characteristics and visual estimates are reproducible. Intracerebral haemorrhage volumes measured using the modified ABC/2 formula were significantly lower compared to standard ABC/2 and computer assisted semi-automatic segmentation. In 629 patients with intracerebral haemorrhage presenting within 48 hours, the effect of blood pressure lowering with transdermal glyceryl trinitrate was assessed. Glyceryl trinitrate lowered blood pressure, was safe but did not improve functional outcome. In a small group of patients treated within 6 hours, glyceryl trinitrate improved functional outcome. Analysis of 246 patients with acute intracerebral haemorrhage from ENOS was undertaken to assess whether there were any differences in functional outcome among those who continued prior antihypertensive drugs during the immediate stroke period compared to those assigned to stop temporarily for 7 days. The results were neutral indicating that there was no benefit in those who continued treatment. Data of 1,011 patients with intracerebral haemorrhage in hyperacute trials from the VISTA collaboration showed differences in baseline characteristics and functional outcomes among patients from various ethnic backgrounds. A systematic review was updated to assess the effect of 26 randomised controlled trials that aimed to alter blood pressure within one week of acute stroke. The results showed that blood pressure reduction did not improve functional outcome irrespective of stroke type. When examined by time, treatment within 6 hours appeared to benefit but the number of patients were small and more studies are needed. The analysis also showed that continuing prestroke antihypertensive drugs in the immediate period after stroke did not benefit and might be harmful. In summary, this thesis provides new information on parameters used to estimate intracerebral haematoma, relationship between management of blood pressure and outcomes after haemorrhagic stroke. The work supports testing of whether very early blood pressure lowering after ictus is beneficial as is being undertaken in ongoing randomised controlled trials. Adjusting for ethnic differences may further identify patients in whom treatment may confer measurable advantage

Neural and cognitive biomarkers of binge and heavy drinking

BACKGROUND: Theories suggest two motivations that drive people to consume alcohol at pathological levels: (1) seeking of short-term pleasurable effects and (2) alleviation of unpleasant states. The former is associated with binge drinking (BD; i.e. high intake during fewer occasions) and the latter with heavy drinking (HD; substantial intake during more occasions). Although direct comparisons have not been made, BD has been associated with impairments in top-down executive control (related to frontal-parietal regions) and HD has been linked to bottom-up changes in internal mentation (related to the default mode network anatomical structure and function). This dissertation compares the two drinking patterns with the goal of testing for differential neurocognitive and neuroanatomical characteristics that would be indicative of two disorder subtypes. METHODS: The sample consisted of adult participants with a history of adolescent onset: BD (N = 16), HD (N = 15), and Healthy Controls (HC; N = 21). All groups were equated on age, education, amount of lifetime alcohol consumed (BD and HD groups), as well as other factors. The study compared group performance on an affective go/no go task and group differences in brain volume and cortical thickness based on structural MRI. RESULTS: Behavioral results showed a higher number of errors for the HD group, in comparison to other groups. Volumetric results indicated a smaller bilateral ventral diencephalon in both BD and HD, in comparison to the HC, and smaller bilateral globus pallidus in BD only. Cortical thickness analyses revealed a thinner left superior parietal region (overlapping with the dorsal attention and fronto-parietal networks) in BD, whereas a left medial occipito-parietal region was thicker in HD (overlapping mainly with the visual network). CONCLUSION: These data, interpreted in the context of prior studies, suggest that BD findings might be indicative of an executive control dysregulation that could contribute to continued BD. HD findings might be indicative of tissue damage due to frequent drinking. Prior research has found the occipital region to have the highest concentration γ-Aminobutyric acid receptors that are affected by alcohol, which might explain the thicker occipital region findings in the HD group

Carotid Artery Disease

This book will bring out the state of art of carotid stenosis in the basic and clinical approaches for better understanding of the mechanisms and useful therapies for these disease. We hope that would be a new current trend understanding new aspects regarding this scientific problematic involving not only anatomical, functional but also clinical questions