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/

New technology in radiological diagnosis: An investigation of diagnostic image quality in digital displays of radiographs

Digital radiology is undergoing rapid evolution. Its objectives can be summarized as the creation within the modern radiology department - and indeed within the entire hospital - of a harmonious, integrated, electronic network capable of handling all diagnostic radiological images, obviating the need for conventional film-based radiology. One of the limiting factors in the introduction and exploitation of digital technology is the issue of image display quality: if electronic display systems are to be widely used for primary radiological diagnosis, it is essential that the diagnostic quality of the displayed images should not be compromised. From the perspective of the practising radiologist, this study examines the performance of the first two commercially available digital radiological display systems to be purchased and installed in a British hospital. This work incorporates an extensive observer performance investigation of image quality from existing 1024- and 1280-line display systems, and suggests that displayed images digitized at a pixel size of 210?m show a significant reduction in diagnostic performance when compared with original film. Such systems appear to be unsuitable for primary radiological diagnosis of subtle lesions. Some of the physical properties of such systems, some relevant methodological issues, and the relationship between image quality and other factors influencing the development acceptance and implementation of digital technology, have also been investigated; the results are presented. This is a controversial subject, and conflicting views have been expressed in the British literature concerning the issue of whether or not the technology is now ready for total system implementation; the view of this author is that careful testing of display systems, and of every other component of digital networks, should precede their entry into clinical use

Evaluation of the utility of specific CXR features for diagnosis of pulmonary tuberculosis in young children using multiple readers

Includes bibliographical referencesINTRODUCTION: The diagnosis of childhood pulmonary tuberculosis (TB) can be notoriously difficult. The chest X-ray (CXR) is a significant diagnostic resource in the detection of PTB in children. However, non-specific radiological features combined with variable inter-observer assessment s contribute to diagnostic uncertainty. The CXR would be of most value when used specifically to evaluate those features of childhood TB that it shows best and where expert observers agree, namely those signs indicating lymphadenopathy. AIM: To identify simple and reliable CXR features of primary TB in children by determining signs and anatomical sites of best observer agreement. METHOD: This is a retrospective descriptive study within a clinical trial performed by the South African TB Vaccine Initiative (SATVI). Healthy BCG-vaccinated newborn infants in a high TB prevalence rural area in Worcester, near Cape Town, South Africa, were followed for a minimum of two years for possible incident al pulmonary TB. Three independent, blinded, expert paediatric radiologists reported the resultant CXR images using a standardised data collection tick sheet, on which the specific anatomical sites and signs of pathology consistent with pulmonary TB were recorded. The first 200 original data collection tick sheets were sampled and recorded in a pre-compiled data spreadsheet for our study. The sampled data were t hen analysed using kappa statistics. RESULTS: The overall combined agreement for airway compression (by presumed lymphadenopathy) was 0.5%. Five % of the CXR's had soft tissue densities reflecting lymphadenopathy on the frontal view and 5% on the lateral view. The most common site reflecting lymphadenopathy through airway narrowing or displacement was the left main bronchus. The hilar region (kappa 0.27) on the frontal CXR and behind bronchus intermedius (kappa 0.18) on the lateral were the most common sites of soft tissue densities reflecting lymphadenopathy. There were no positive findings for cavitation or pleural effusion. The overall decisions reflecting PTB (lymphadenopathy or miliary) by each individual reader were 27.6% by Reader 1, 8.5% by Reader 2 and 24.6 % by Reader 3. Abnormal findings not specific for PTB were found in 3.5 % by Reader 1, 10.5% by Reader 2 and 3.5% by Reader 3.68. 3 % of the radiographs were reported as normal by Reader 1, 81.9% by Reader 2 and 66.8 % by Reader 3. Only 5% of the radiographs were found to be unreadable by one reader. The overall agreement of all three readers on PTB was 14.6 % and for normal CXR 49.2%. CONCLUSIONS: The fair degree of agreement amongst expert readers suggests that the CXR alone is not a reliable tool for detecting pulmonary TB and should be utilised in conjunction with the clinical features and/or skin tests and blood results. Soft tissue masses rather than airway compression are a more reliable sign for lymphadenopathy, with the most agreed upon sites on the frontal projection for soft tissue mass detection being the right hilar region, followed by the left hilum. Unfortunately, this study could not confirm the usefulness of the CXR in subcategorising PTB into severe and non-severe groups due to the absence of any positive features for severe PTB in the selected sample. The use of prescribed tick-sheets with specified features for detecting lymphadenopathy did not have the expected impact of promoting interobserver consensus of CXR findings in children in terms of detection of TB. The absence of a credible reference standard for lymphadenopathy remains a significant limitation

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

Advancements in Polymer Electrolyte Fuel Cell Architecture and Performance using Electrochemical Modelling and Advanced Characterisations

With the ever depleting traditional energy sources and increasing the carbon footprints, the new landscape of the renewable energy sources has evolved. With the versatility of required environmental conditions, topological locations, operating temperature, polymer electrolyte fuel cells (PEFCs) operating on hydrogen has been recognised as a prominent renewable energy technology. PEFCs offers the possibility of zero-emission and high power density electricity generation for a wide range of transport, portable, and stationary power applications. While technology continues to improve, there are still some challenges concerning durability, cost and performance. An improved understanding of the processes occurring within operational fuel cells and optimisation of the cell architecture will accelerate large-scale commercialization of PEFCs. The most powerful ways to understand and resolve these challenges is to understand the complex interplay of the internal workings of fuel cells and cell design and architecture and operating conditions. Hence, the current research aims to analyse the advancements in the fuel cell design and architecture using a thermo-structural multiphase electrochemical modelling and the advanced characterisation techniques Firstly, the intricate relationship between cell compression and the flow-field architecture is established by determining the morphological factors using X-ray computed tomography (CT) techniques. The results provide insight into the complex interplay of the morphological factors deciding fuel cell performance and durability. Also, this study provides insight into the extent at which the morphological factors decide water and thermal management of the fuel cell, which are key issues to tackle to broad-scale commercialisation of the technology. Further, the multiphase non-isothermal two-dimensional numerical model was developed. The two-dimensional current, temperature and liquid water saturation profiles reveal the in-situ gradients and their correlations with the voltage decay with respect to an increase in cell compression. Finally, the effects of cell compression on the PEFC water dynamics were analysed using in-plane and through-plane in-operando neutron radiography. Neutron radiography provides a detailed understanding of what constitutes the thickness of liquid water present in the operating fuel cell. The Neutron radiography results were also used to validate the numerical models developed. Finally, this work also investigates the effect of secondary flow-field on the dead-ended anode performance and highlights the importance of the manufacturing and assembly tolerances on fuel cell efficiency. Collectively; this project delineates the comprehensive suite of characterisation techniques and numerical modelling to resolve the PEFC challenges and achieve the cell optimisation and durability required for wide-scale commercialisation of the technology

Effects of patient recumbency position on neonatal chest EIT

This paper investigates the overlooked effects of the patient recumbency positions on one of the key clinically used parameters in chest electrical impedance tomography (EIT) monitoring – the silent spaces. This parameter could impact medical decisions and interventions by indicating how well each lung is being ventilated. Yet it is largely dependent on assumptions of prior model at the reconstruction stage and the closely linked region of interest (ROI) during the final calculations. The potential effect of switching recumbency modes on silent spaces as a results of internal organ movements and consequently changes in initial assumptions, has been studied. The displacement and deformations caused by posture changes from supine to lateral recumbent were evaluated via simulations considering the simultaneous gravity-dependent movement and/or deformations of heart, mediastinum, lungs and the diaphragm. The reliability of simulations was verified against reference radiography images of an 18-month-old infant in supine and decubitus lateral positions. Inspecting a set of 10 patients from age range of 1 to 2 years old revealed improvements of up to 30% in the silent space parameters when applying posture consistent amendments as opposed to fixed model/ROI to each individual. To minimize the influence of image reconstruction technique on the results two different EIT reconstruction algorithms were implemented. The outcome emphasized the importance of including recumbency situation during chest EIT monitoring within the considered age range

Effects of Patient Recumbency Position on Neonatal Chest EIT

This paper investigates the overlooked effects of the patient recumbency positions on one of the key clinically used parameters in chest electrical impedance tomography (EIT) monitoring – the silent spaces. This parameter could impact medical decisions and interventions by indicating how well each lung is being ventilated. Yet it is largely dependent on assumptions of prior model at the reconstruction stage and the closely linked region of interest (ROI) during the final calculations. The potential effect of switching recumbency modes on silent spaces as a results of internal organ movements and consequently changes in initial assumptions, has been studied. The displacement and deformations caused by posture changes from supine to lateral recumbent were evaluated via simulations considering the simultaneous gravity-dependent movement and/or deformations of heart, mediastinum, lungs and the diaphragm. The reliability of simulations was verified against reference radiography images of an 18-month-old infant in supine and decubitus lateral positions. Inspecting a set of 10 patients from age range of 1 to 2 years old revealed improvements of up to 30% in the silent space parameters when applying posture consistent amendments as opposed to fixed model/ROI to each individual. To minimize the influence of image reconstruction technique on the results two different EIT reconstruction algorithms were implemented. The outcome emphasized the importance of including recumbency situation during chest EIT monitoring within the considered age range