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

Statement on imaging and pulmonary hypertension from the Pulmonary Vascular Research Institute (PVRI)

Pulmonary hypertension (PH) is highly heterogeneous and despite treatment advances it remains a life-shortening condition. There have been significant advances in imaging technologies, but despite evidence of their potential clinical utility, practice remains variable, dependent in part on imaging availability and expertise. This statement summarizes current and emerging imaging modalities and their potential role in the diagnosis and assessment of suspected PH. It also includes a review of commonly encountered clinical and radiological scenarios, and imaging and modeling-based biomarkers. An expert panel was formed including clinicians, radiologists, imaging scientists, and computational modelers. Section editors generated a series of summary statements based on a review of the literature and professional experience and, following consensus review, a diagnostic algorithm and 55 statements were agreed. The diagnostic algorithm and summary statements emphasize the key role and added value of imaging in the diagnosis and assessment of PH and highlight areas requiring further research

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS)

Peer reviewe

Novel approaches to the assessment of patients with chest systoms in the acute medical and outpatient settings: the use of multislice computed tomography

This thesis evaluated the clinical utility of cardiopulmonary computed tomography (CT) in patients presenting with chest pain and dyspnoea. Studies within this thesis confirmed the following. Firstly, there is a requirement for improved diagnostic pathways to minimise patients being discharged without a diagnosis, which currently occurs in 30-40% of patients admitted with chest pain and dyspnoea. Historically, CT has been utilised in 32% of admissions with chest pain and 10% of admissions with dyspnoea. Secondly, challenges exist to the wider adoption of cardiopulmonary CT. These include patient-related factors, institutional capabilities and guideline restrictions. In acute admissions, 11% of patients with dyspnoea and 7% of patients with chest pain and a low to moderate likelihood of CAD are suitable for CT. In the RACPC setting, including patients across the entire spectrum of CAD likelihood, 18% of patients are suitable for CT. NICE CG95 would recommend only 1% of acute chest pain admissions and 2% of RACPC attenders for CT. Thirdly, NICE CG95 would recommend 51% of acute chest pain admissions and 66% of RACPC attenders for discharge without cardiac investigation. In the RACPC population, significant CAD is identified in 10% of these patients and a major adverse cardiac event in 2%. Fourthly, in selected patients with suspected cardiac chest pain, cardiac CT has a diagnostic yield of 21% in acute admissions and 13% in RACPC attenders for significant CAD. In acute admissions with dyspnoea, cardiopulmonary CT has a diagnostic yield of 20% for CAD, 20% for pulmonary embolism, nil for aortic dissection and 89% for non-vascular chest pathology. Fifthly, inclusion of CT in diagnostic pathways for chest pain result in fewer patients discharged without a diagnosis, fewer invasive angiography procedures and reduced diagnostic costs. In patients with dyspnoea, CT provides value to clinicians making diagnoses and supports early discharge without detrimental outcomes.Open Acces