PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

A robust technique for the detection and quantification of abdominal aortic calcification using dual energy X-Ray absorptiometry

Arterial calcification is a manifestation of atherosclerosis, which over the last two decades has become a recognised predictor of cardiovascular disease. Abdominal Aortic Calcification (AAC) and osteoporosis have been shown to coincide in older individuals. The accepted method of diagnosing osteoporosis is through the measurement of bone mineral density by dual energy x-ray absorptiometry (DXA). Vertebral fracture assessment (VFA) images obtained alongside BMD using DXA technology provide an inexpensive resource for AAC diagnosis. Although several simple methods have been proposed for manual semi-quantitative scoring of AAC in x-ray images in the past, these methods have limitations in terms of capturing small changes in atherosclerosis progression and are time-consuming. Several automatic approaches have been proposed to measure AAC on radiographs. However, these methods have not been related to any accepted medical AAC scoring systems and thus are not likely to be adopted easily by the medical community. In addition, there has been no attempt to apply the proposed methods to VFA images. The main focus of the research presented in this thesis is the automatic quantification of AAC in VFA images acquired in single energy mode. The thesis is divided into two main parts. In the first part, an automatic method for AAC detection and quantification in VFA images is proposed and evaluated on a large number of images. In the second part, the performance of both single and dual energy VFA imaging for the detection of uniformly distributed calcification is investigated. The automatic method for AAC detection consists of two stages. In the first stage an active appearance model was employed for the purpose of segmentaion. In the second stage, adaptive thresholding techniques were used to detect AAC, whilst automatic iii classification techniques were used to quantify the detected calcification. The performance of several classifiers were investigated, and the proposed method was evaluated against the manual AC-24 scoring method using several hundred images and two human readers. A thorough statistical analysis of the results showed that, overall, the SVM classifier gave the best results. Weighted accuracy, sensitivity, specificity assessed for 4 AAC categories were 89.2%, 78.5% and 92.3% respectively while the corresponding values for 3 AAC categories were 88.6%, 86%, 90.4%. In the second part, a study using a tissue-mimicking physical phantom is described. The phantom consists of an aluminium strip within Perspex to simulate calcification and abdominal soft tissue respectively. VFA images of different phantom configurations were acquired in single energy (SE) and dual energy (DE) modes. The minimum detectable aluminium thickness was assessed visually and related to contrast and contrast-to-noise ratio. Percentage coefficient of variation was used to quantify uniformity, repeatability and reproducibility with a Perspex width of 25 cm, the smallest thickness of aluminium that could be detected was 0.20- 0.25 mm. The initial results are promising, and the system proposed in this research can be used as an alternative method to the manual scoring system (AC-24) for a wide range of AAC. The principal conclusion from the phantom work is that under idealised imaging conditions, VFA images have the potential to be used for detecting small thicknesses of calcification with good linearity, repeatability and reproducibility in SE and DE modes for patients with a body width < 30 cm

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

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

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Apport de l'assistance par ordinateur lors de la pose d'endoprothèse aortique

The development of endovascular aortic procedures is growing. These mini-invasive techniques allow a reduction of surgical trauma, usually important in conventional open surgery. The technical limitations of endovascular repair are pushed to special aortic localizations which were in the past decade indication for open repair. Success and efficiency of such procedures are based on the development and the implementation of decision-making tools. This work aims to improve endovascular procedures thanks to a better utilization of pre and intraoperative imaging. This approach is in the line with the framework of computer-assisted surgery whose concepts are applied to vascular surgery. The optimization of endograft deployment is considered in three steps. The first part is dedicated to preoperative imaging analysis and shows the limits of the current sizing tools. The accuracy of a new measurement criterion is assessed (outer curvature length). The second part deals with intraoperative imaging and shows the contribution of augmented reality in endovascular aortic repair. In the last part, image guided surgery on soft tissues is addressed, especially the arterial deformations occurring during endovascular procedures which disprove rigid registration in fusion imaging. The use of finite element simulation to deal with this issue is presented. We report an original approach based on a predictive model of deformations using finite element simulation with geometrical and anatomo-mechanical patient specific parameters extracted from the preoperative CT-scan.Les techniques endovasculaires, particulièrement pour l’aorte, sont en plein essor en chirurgie vasculaire. Ces techniques mini-invasives permettent de diminuer l’agression chirurgicale habituellement importante lors de la chirurgie conventionnelle. Les limites techniques sont repoussées à certaines localisations de l’aorte qui étaient il y a encore peu de temps inaccessibles aux endoprothèses. Le succès et l’efficience de ces interventions reposent en partie sur l'élaboration et la mise en œuvre de nouveaux outils d'aide à la décision. Ce travail entend contribuer à l’amélioration des procédures interventionnelles aortiques grâce à une meilleure exploitation de l’imagerie pré et peropératoire. Cette démarche s’inscrit dans le cadre plus général des Gestes Médico-Chirurgicaux Assistés par Ordinateur, dont les concepts sont revisités pour les transposer au domaine de la chirurgie endovasculaire. Trois axes sont développés afin de sécuriser et optimiser la pose d'endoprothèse. Le premier est focalisé sur l’analyse préopératoire du scanner (sizing) et montre les limites des outils de mesure actuels et évalue la précision d’un nouveau critère de mesure des longueurs de l’aorte (courbure externe). Le deuxième axe se positionne sur le versant peropératoire et montre la contribution de la réalité augmentée dans la pose d’une endoprothèse aortique. Le troisième axe s’intéresse au problème plus général des interventions sur les tissus mous et particulièrement aux déformations artérielles qui surviennent au cours des procédures interventionnelles qui mettent en défaut le recalage rigide lors de la fusion d’images. Nous présentons une approche originale basée sur un modèle numérique de prédiction des déformations qui utilise la simulation par éléments finis en y intégrant des paramètres géométriques et anatomo-mécaniques spécifique-patient extraits du scanner préopératoire

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