Highlighting dissimilarity in medical images using hierarchical clustering based segmentation (HCS).

Tissue abnormality in a medical image is usually related to a dissimilar part of an otherwise homogeneous image. The dissimilarity may be subtle or strong depending on the medical modality and the type of abnormal tissue. Dissimilarity within an otherwise homogeneous area of an image may not always be due to tissue abnormality. It might be due to image noise or due to variability within the same tissue type. Given this situation it is almost impossible to design and implement a generic segmentation process that will consistently give a single appropriate solution under all conditions. Hence a dissimilarity highlighting process that yields a hierarchy of segmentation results is more useful. This would benefit from high level human interaction to select the appropriate image segmentation for a particular application, because one of the capabilities of the human vision process when visualising images is its ability to visualise them at different levels of details.The purpose of this thesis is to design and implement a segmentation procedure to resemble the capability of the human vision system's ability to generate multiple solutions of varying resolutions. To this end, the main objectives for this study were: (i) to design a segmentation process that would be unsupervised and completely data driven. (ii) to design a segmentation process that would automatically and consistently generate a hierarchy of segmentation results. In order to achieve these objectives a hierarchical clustering based segmentation (HCS) process was designed and implemented. The developed HCS process partitioned the images into their constituent regions at hierarchical levels of allowable dissimilarity between the different spatially adjacent or disjoint regions. At any particular level in the hierarchy the segmentation process clustered together all the pixels and/or regions that had dissimilarity among them which was less than or equal to the dissimilarity allowed for that level. The clustering process was designed in such a way that the merging of the clusters did not depend on the order in which the clusters were evaluated.The HCS process developed was used to process images of different medical modalities and the results obtained are summarised below: (i) It was successfully used to highlight hard to visualise stroke affected areas in T2 weighted MR images confirmed by the diffusion weighted scans of the same areas of the brain. (ii) It was used to highlight dissimilarities in the MRI, CT and ultrasound images and the results were validated by the radiologists. It processed medical image data and consistently produced a hierarchy of segmentation results but did not give a diagnosis. This was left for the experts to make use of the results and incorporate these with their own knowledge to arrive upon a diagnosis. Thus the process acts as an effective computer aided detection (CAD) tool.The unique features of the designed and implemented HCS process are: (i) The segmentation process is unsupervised, completely data driven and can be applied to any medical modality, with equal success, without any prior information about the image data(ii) The merging routines can evaluate and merge spatially adjacent and disjoint similar regions and consistently give a hierarchy of segmentation results. (iii) The designed merging process can yield crisp border delineation between the regions

DEVELOPING INTEGRATED MACHINE LEARNING MODELS FOR AUTOMATIC COMPUTER-AIDED DIAGNOSIS IN ISCHEMIC ACUTE STROKE MRI

Fast detection and quantification of lesion cores in diffusion weighted images (DWIs) has been highly anticipated in clinical and research communities for planning treatment of acute stroke. The recent emergence of successful machine learning (ML) methods, especially Deep Learning (DL), enables automatic Computer Aided Diagnosis (CAD) of stroke in DWIs. However, the lack of publicly available large-scale data and ML models in clinical acute stroke DWI application are still the bottlenecks. In this work, we established the first large annotated open-source database of 2,888 clinical acute stroke MRIs (Chapter 2) to train and develop ML models for automatic stroke lesion detection and segmentation in clinical acute stroke MRI (Chapter 3). For automatic measurement of infarcted arterial territories, the first digital 3D deformable brain arterial territory atlas was created (Chapter 4). In addition, a fully automatic ML system is created to generate automatic radiological reports (Chapter 5 and 6) for calculation of ASPECTS, prediction and quantification of infarcted arterial and anatomical regions, and estimation of hydrocephalus presented in acute stroke MRI. The complete ML system in this work runs locally in real time with minimal computational requirements. It is publicly available and readily useful for non-expert users

Machine Learning in Medical Image Analysis

Machine learning is playing a pivotal role in medical image analysis. Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems. Particularly, with the wide application of deep learning approaches, the performance of medical image analysis has been significantly improved. In this thesis, we investigate machine learning methods for two key challenges in medical image analysis: The first one is segmentation of medical images. The second one is learning with weak supervision in the context of medical imaging. The first main contribution of the thesis is a series of novel approaches for image segmentation. First, we propose a framework based on multi-scale image patches and random forests to segment small vessel disease (SVD) lesions on computed tomography (CT) images. This framework is validated in terms of spatial similarity, estimated lesion volumes, visual score ratings and was compared with human experts. The results showed that the proposed framework performs as well as human experts. Second, we propose a generic convolutional neural network (CNN) architecture called the DRINet for medical image segmentation. The DRINet approach is robust in three different types of segmentation tasks, which are multi-class cerebrospinal fluid (CSF) segmentation on brain CT images, multi-organ segmentation on abdomen CT images, and multi-class tumour segmentation on brain magnetic resonance (MR) images. Finally, we propose a CNN-based framework to segment acute ischemic lesions on diffusion weighted (DW)-MR images, where the lesions are highly variable in terms of position, shape, and size. Promising results were achieved on a large clinical dataset. The second main contribution of the thesis is two novel strategies for learning with weak supervision. First, we propose a novel strategy called context restoration to make use of the images without annotations. The context restoration strategy is a proxy learning process based on the CNN, which extracts semantic features from images without using annotations. It was validated on classification, localization, and segmentation problems and was superior to existing strategies. Second, we propose a patch-based framework using multi-instance learning to distinguish normal and abnormal SVD on CT images, where there are only coarse-grained labels available. Our framework was observed to work better than classic methods and clinical practice.Open Acces

Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities

Cerebrovascular dysfunction in cerebral small vessel disease

INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

Added value of acute multimodal CT-based imaging (MCTI) : a comprehensive analysis

Introduction: MCTI is used to assess acute ischemic stroke (AIS) patients.We postulated that use of MCTI improves patient outcome regardingindependence and mortality.Methods: From the ASTRAL registry, all patients with an AIS and a non-contrast-CT (NCCT), angio-CT (CTA) or perfusion-CT (CTP) within24 h from onset were included. Demographic, clinical, biological, radio-logical, and follow-up caracteristics were collected. Significant predictorsof MCTI use were fitted in a multivariate analysis. Patients undergoingCTA or CTA&CTP were compared with NCCT patients with regards tofavourable outcome (mRS ≤ 2) at 3 months, 12 months mortality, strokemechanism, short-term renal function, use of ancillary diagnostic tests,duration of hospitalization and 12 months stroke recurrence

Imaging intracranial arterial patency and intravenous thrombolysis in acute ischaemic stroke

Among patients presenting acutely with ischaemic stroke who are being considered for intravenous thrombolysis, prompt brain imaging is used to exclude contraindications to treatment (chiefly haemorrhagic stroke or other conditions mimicking stroke) rather than to identify which patients are more or less likely to benefit from thrombolysis. For example, it is unclear whether the presence or absence of arterial obstruction on imaging should be used to guide thrombolysis treatment decisions. In this thesis I explore methods of imaging arterial patency among patients presenting acutely with ischaemic stroke and look for associations between these early imaging findings, response to intravenous thrombolysis and functional outcome six-months after stroke onset. I primarily use data from the Third International Stroke Trial (IST-3), the largest ever randomised-controlled trial testing the use of intravenous alteplase for the acute treatment of ischaemic stroke. I begin by summarising the main features of stroke, covering techniques for imaging the brain and for imaging arterial patency, and post-stroke outcomes. Next I describe two literature reviews which I compiled to increase my understanding of the topic with particular reference to imaging arterial patency. This is followed by a summary of IST-3. Then I describe the general methods I used to address my thesis aims exploring relationships between imaging characteristics of arterial patency, treatment with intravenous alteplase and functional outcome after ischaemic stroke. Specifically, I investigated the following imaging features: - The hyperattenuating artery sign (HAS), which is a non-contrast enhanced CT finding thought to be indicative of acute arterial obstruction by thrombus or embolus - Arterial patency or obstruction as demonstrated using contrast enhanced CT and MR angiographic imaging. In addition to providing better characterisation of the HAS and a better understanding of how angiography helps to assess ischaemic stroke patients, I found that arterial obstruction (however this is identified on imaging) is associated with more severe stroke at baseline and worse functional outcome six months after stroke. I also prove that intravenous alteplase is effective in the presence of arterial obstruction, counter to a widely held concern that it may not be effective in this context. Most of my work has been published in peer reviewed journals. My work should give front line clinicians greater confidence to use intravenous alteplase for the treatment of ischaemic stroke associated with arterial obstruction on imaging, but more work is needed to better understand the implications of apparently normal arterial patency on imaging among patients with ischaemic stroke

Applications of Artificial Intelligence in Medicine Practice

This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted