Adaptive Radiation Therapy for Head and Neck Cancer—Can an Old Goal Evolve into a New Standard?

Current head and neck intensity-modulated radiotherapy (IMRT) techniques cause significant toxicity. This may be explained in part by the fact that IMRT cannot compensate for changes in the location of disease and normal anatomy during treatment, leading to exposure of at-risk bystander tissues to higher-than-anticipated doses. Adaptive radiotherapy (ART) is a novel approach to correct for daily tumor and normal tissue variations through online or offline modification of original IMRT target volumes and plans. ART has been discussed on a conceptual level for many years, but technical limitations have hampered its integration into routine care. In this paper, we review the key anatomic, dosimetric, and treatment delivery issues at play in current investigational development of head and neck ART. We also describe pilot findings from initial clinical deployment of head and neck ART, as well as emerging pathways of future research

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Translation of quantitative MRI analysis tools for clinical neuroradiology application

Quantification of imaging features can assist radiologists by reducing subjectivity, aiding detection of subtle pathology, and increasing reporting consistency. Translation of quantitative image analysis techniques to clinical use is currently uncommon and challenging. This thesis explores translation of quantitative imaging support tools for clinical neuroradiology use. I have proposed a translational framework for development of quantitative imaging tools, using dementia as an exemplar application. This framework emphasises the importance of clinical validation, which is not currently prioritised. Aspects of the framework were then applied to four disease areas: hippocampal sclerosis (HS) as a cause of epilepsy; dementia; multiple sclerosis (MS) and gliomas. A clinical validation study for an HS quantitative report showed that when image interpreters used the report, they were more accurate and confident in their assessments, particularly for challenging bilateral cases. A similar clinical validation study for a dementia reporting tool found improved sensitivity for all image interpreters and increased assessment accuracy for consultant radiologists. These studies indicated benefits from quantitative reports that contextualise a patient’s results with appropriate normative reference data. For MS, I addressed a technical translational challenge by applying lesion and brain quantification tools to standard clinical image acquisitions which do not include a conventional T1-weighted sequence. Results were consistent with those from conventional sequence inputs and therefore I pursued this concept to establish a clinically applicable normative reference dataset for development of a quantitative reporting tool for clinical use. I focused on current radiology reporting of gliomas to establish which features are commonly missed and may be important for clinical management decisions. This informs both the potential utility of a quantitative report for gliomas and its design and content. I have identified numerous translational challenges for quantitative reporting and explored aspects of how to address these for several applications across clinical neuroradiology

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging

Worldwide, the prevalence of cardiovascular diseases has doubled, demanding new diagnostic tools. Artificial intelligence, especially machine learning and deep learning, offers innovative possibilities for medical research. Despite historical challenges, such as a lack of data, these techniques have potential for cardiovascular research. This thesis explores the application of machine learning and deep learning in cardiology, focusing on automation and decision support in cardiovascular imaging.Part I of this thesis focuses on automating cardiovascular MRI analysis. A deep learning model was developed to analyze the ascending aorta in cardiovascular MRI images. The model's results were used to investigate connections between genetic material and aortic properties, and between aortic properties and cardiovascular diseases and mortality. A second model was developed to select MRI images suitable for analyzing the pulmonary artery.Part II focuses on decision support in nuclear cardiovascular imaging. A first machine learning model was developed to predict myocardial ischemia based on CTA variables. In addition, a deep neural network was used to identify reduced oxygen supply through the arteries supplying oxygen-rich blood to the heart and cardiovascular risk features using PET images.This thesis successfully explores the possibilities of machine learning and deep learning in cardiovascular research, with a focus on automated analysis and decision support

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging

Worldwide, the prevalence of cardiovascular diseases has doubled, demanding new diagnostic tools. Artificial intelligence, especially machine learning and deep learning, offers innovative possibilities for medical research. Despite historical challenges, such as a lack of data, these techniques have potential for cardiovascular research. This thesis explores the application of machine learning and deep learning in cardiology, focusing on automation and decision support in cardiovascular imaging.Part I of this thesis focuses on automating cardiovascular MRI analysis. A deep learning model was developed to analyze the ascending aorta in cardiovascular MRI images. The model's results were used to investigate connections between genetic material and aortic properties, and between aortic properties and cardiovascular diseases and mortality. A second model was developed to select MRI images suitable for analyzing the pulmonary artery.Part II focuses on decision support in nuclear cardiovascular imaging. A first machine learning model was developed to predict myocardial ischemia based on CTA variables. In addition, a deep neural network was used to identify reduced oxygen supply through the arteries supplying oxygen-rich blood to the heart and cardiovascular risk features using PET images.This thesis successfully explores the possibilities of machine learning and deep learning in cardiovascular research, with a focus on automated analysis and decision support