Analyse automatique de radiographies pulmonaires pour le diagnostic précoce du syndrome de détresse respiratoire aiguë

RÉSUMÉ Le Syndrome de Détresse Respiratoire Aiguë (SDRA) est une maladie pulmonaire qui représente la forme la plus grave de l’insuffisance respiratoire aiguë. Elle consiste en une atteinte inflammatoire aiguë des poumons et se manifeste au niveau de la radiographie pulmonaire sous forme d’opacités bilatérales. Le diagnostic de cette maladie est effectué à partir de données sur l’hypoxémie et de l’analyse de la radiographie pulmonaire. L’interprétation de la radiographie pulmonaire par des experts souffre d’une variabilité inter-observateurs élevée, ce qui peut entraîner un diagnostic tardif. Cela est problématique, car un diagnostic retardé d’un patient atteint du SDRA rend son traitement moins efficace et peut, par conséquent, grever son pronostic. D’où l’intérêt de développer un système d’aide à la décision clinique (SADC) pour aider le médecin à établir un diagnostic précoce de la maladie. Les SADC pour le diagnostic automatique de maladies à partir des radiographies sont devenus des outils très importants. Ils consistent à analyser automatiquement les radiographies pour identifier les anomalies et procurer un deuxième avis diagnostic aux médecins. Même si plusieurs SADC ont été déjà développés, il n’existe aucun SADC pour le diagnostic du SDRA. La difficulté principale est due à la superposition de structures osseuses telle que la cage thoracique, dont la propriété de radio-opacité rend leur apparence très similaire à celle des opacités diffuses liées au SDRA dans la radiographie pulmonaire. Une segmentation préalable des côtes entières est requise dans le but de les exclure de l’analyse et de focaliser sur l’étude des opacités diffuses due au SDRA. Pour pouvoir valider un SADC pour le diagnostic du SDRA, la création d’une base de radiographies pulmonaires diagnostiquées avec précision est aussi indispensable. La première partie de cette thèse propose un SADC original pour le diagnostic du SDRA à partir de radiographies pulmonaires. Comme il n’existe actuellement aucun système dédié pour cette pathologie, il a fallu le construire de novo. Le SADC développé consiste à analyser une radiographie du thorax après soustraction des côtes entières (postérieures et antérieures) pour pouvoir extraire des régions d’intérêt (ROI) intercostales qui se composent de tissus pulmonaires et analyser uniquement ces régions. Des caractéristiques statistiques et spectrales sont extraites pour chaque ROI. Ensuite, une méthode de transformation des caractéristiques est appliquée en utilisant l’analyse discriminante linéaire (Linear Discriminant Analysis). Les ROI sont ensuite classifiées comme normales ou anormales en utilisant un classifieur SVM. Finalement, le pourcentage des ROI anormales est calculé pour chaque cadran (chaque poumon est divisé en deux parties appelées cadrans). Si ce pourcentage est supérieur à 34%, le cadran donné est alors considéré comme touché. Et si au moins un cadran du poumon gauche et un cadran du poumon droit sont touchés, alors la radiographie pulmonaire est considérée comme un cas de SDRA. Le SADC proposé a été évalué en utilisant une base de radiographies pulmonaires diagnostiquées avec consensus entre plusieurs experts et des mesures de performance telles que la sensibilité et la spécificité ont été calculées. Le système automatisé développé pour le diagnostic du SDRA a permis d’obtenir une bonne sensibilité et une bonne spécificité (sensibilité = 90.6% et spécificité = 86.5%). La deuxième partie de cette thèse présente une étude sur la variabilité inter-observateurs pour le diagnostic du SDRA à partir de radiographies pulmonaires en utilisant notre système, soit individuellement, soit comme deuxième avis. Cette étude a été réalisée en calculant le coefficient Kappa, d’abord entre les experts, ensuite en utilisant le système d’analyse développé. Notre système d’analyse automatique a permis d’améliorer le coefficient Kappa et d’obtenir une bonne concordance de diagnostic en l’utilisant individuellement (Kappa = 0.77) ainsi qu’une meilleure concordance ou une concordance presque parfaite de diagnostic en l’utilisant comme deuxième avis (Kappa = 0.79-0.86). La troisième partie de cette thèse est consacrée à une analyse des besoins pour un SADC déployable en clinique. Dans cette étude, nous avons remarqué que peu de SADC pour l’interprétation de radiographies pulmonaires ont été commercialisés. Aussi, nous avons montré que plusieurs facteurs doivent être considérés pour développer un SADC en soins intensifs. Ces facteurs incluent : la segmentation interactive pour l’extraction des régions d’intérêt (ROI) afin d’améliorer la performance; le choix des caractéristiques devrait être basé sur les différents aspects qui caractérisent l’apparence de la pathologie sur les radiographies pulmonaires et devraient être combinées pour atteindre une meilleure performance; et la construction de la base de données pour la validation du système joue un rôle très important dans la performance de tout SADC. Ce dernier facteur implique que la base des radiographies pulmonaires doit être construite avec précaution en considérant les facteurs suivants: le nombre de radiographies normales et anormales à utiliser ainsi que la représentativité de la diversité des anomalies; la méthodologie à utiliser pour élaborer l’interprétation des radiographies pulmonaires; et la qualité des images à utiliser. Finalement, la création de bases de radiographies pulmonaires publiques permettrait de comparer différents SADC et de choisir celui ayant la meilleure performance, et par conséquent celui qui doit être testé en premier en clinique. En conclusion, ce projet propose un SADC pour le diagnostic précoce du SDRA à partir de radiographies pulmonaires. Son évaluation a permis de confirmer qu’il peut être utilisé par les médecins pour fournir un deuxième avis afin d’élaborer un diagnostic plus précis. En perspective, pour utiliser notre système tout au long du traitement d’un patient atteint du SDRA, une fusion multimodale d’images (RX/ CT/ TIE) permettrait de visualiser à la fois l’information fonctionnelle et l’information morphologique, ainsi que de connaître l’état actuel du patient. Ceci donnerait lieu à un suivi clinique plus efficace au chevet du patient, entre autres en choisissant les paramètres optimaux pour la ventilation mécanique.----------ABSTRACT Acute Respiratory Distress Syndrome (ARDS) is a lung disease which represents the most severe form of acute respiratory failure. It consists of an acute inflammation of the lungs and manifests as bilateral opacities in chest radiographs. The diagnosis of this disease is done using the chest X-ray and hypoxemia criteria. However, interpretation of chest X-ray by medical experts suffers from high inter-observer variability, which can lead to a delayed diagnosis. This is problematic because any delay in diagnosing ARDS makes its treatment less effective and may, therefore, burden the patient’s prognosis. Hence, there is a clear clinical motivation to develop a computer-aided diagnosis (CAD) system to help the doctor establish an early chest X-ray diagnosis of the disease. CAD systems using X-rays for automatic diagnosis of diseases have become very important tools. They consist in automatically analyzing radiographs to identify abnormalities and providing a second diagnostic opinion to physicians. Even though several CAD systems have already been developed, there is currently no such system for the diagnosis of ARDS. The main difficulty is due to the superposition of bone structures such as the rib cage, whose radiopacity makes their appearance very similar to that of diffuse opacities associated with ARDS in chest radiographs. A preliminary segmentation of the whole ribs is thereby required in order to exclude them from the analysis and to focus on studying only the diffuse opacities linked with ARDS. To validate a CAD system for diagnosing ARDS, the creation of a database of chest X-rays diagnosed accurately is also essential. The first part of this thesis proposes a novel CAD system for the diagnosis of ARDS from chest radiographs. As there is currently no dedicated system for this disease, it was necessary to build it de novo. The CAD system we developed consists in analyzing a chest radiograph by first subtracting the whole ribs (anterior and posterior) from the image, and subsequently extracting intercostal patches and analyzing these regions of interest only, which are made up of lung tissues. Statistical and spectral features are extracted from each patch. A feature transformation method is then applied using Linear Discriminant Analysis (LDA). The patches are then classified as either normal or abnormal using an SVM classifier. Finally, the rate of abnormal patches is calculated for each quadrant (each lung is divided into two parts called quadrants). If this rate is greater than 34%, the given quadrant is then considered as affected. And if at least one quadrant of the left lung and one of the right lung are affected, then the chest radiograph is considered as an ARDS case. The proposed CAD system was evaluated using a database of chest radiographs diagnosed with consensus among several experts, and performance measurements such as sensitivity and specificity were calculated. The automated system developed for diagnosing ARDS achieved a sensitivity and specificity that are both good (sensitivity = 90.6% and specificity = 86.5%). The second part of this thesis presents a study of the inter-observer variability for the diagnosis of ARDS from chest radiographs using our system either by itself or as providing a second opinion. This study was carried out by calculating the Kappa coefficient, first between the medical experts, then by using the proposed CAD system. Our automatic analysis system improved the Kappa coefficient and showed a good diagnostic agreement when used individually (Kappa = 0.77) and a better diagnostic agreement or an almost perfect agreement diagnosis using it to give a second opinion (kappa = 0.79-0.86). The third part of this thesis is devoted to a requirements analysis for a CAD system to be used in the clinical setting. In this study, we noticed that only a few CAD systems for chest X-ray interpretation are commercially available. Thus, we showed that several factors must be considered when developing a CAD system for use in intensive care. These factors include: interactive segmentation for extracting regions of interest (ROI) to improve performance; the choice of features should be based on the different aspects that characterize the appearance of the pathology in chest X-rays and should be combined to achieve better performance; and the construction of a validation database, which plays a very important role in the performance of any CAD system. The latter implies that the database must be carefully constructed by considering the following factors: the number of normal and abnormal chest X-rays to be used and the representativeness of the diversity of abnormalities, the methodology used to interpret the chest X-rays, and the quality of the images to use. Finally, the creation of public databases of pulmonary radiographs would make it easier to compare different CAD systems and to choose the one that performs best and therefore the one to be tested first in the clinical setting. In conclusion, this project proposes a CAD system for the early diagnosis of ARDS from chest X-rays. Its evaluation allowed us to confirm that it can be used by doctors to provide a second opinion with the aim of elaborating a more accurate diagnosis. In future work, to utilize our system throughout the treatment of an ARDS patient, a multimodal image fusion approach (RX / CT / EIT) would allow the visualization of both functional and morphological information, as well as knowing the patient's current condition. This would give rise to more efficient monitoring at the patient’s bedside, in particular by choosing the optimal settings for mechanical ventilation

Detection of Infiltrate on Infant Chest X-Ray

Currently, Chest X-ray is still widely used around the world for disease examination. This is due to its low cost, low radiation and a lot of disease information. The commonly detected disease using chest x-rays is lung disease. The characteristic of this disease is infiltrate. However, the accuracy of Chest X-ray observations is still low. Therefore, this research offers a method to perform Chest X-ray image processing in clarifying the information contained therein. This research used Chest X-ray of infant patients who treated at Central Public Hospital (RSUP) Dr. M. Djamil Padang. The total of the images tested were 17 images. In these images, there were some suspected infiltrates after being analyzed by doctors. Software used was Matlab which is conducted by applying image processing method. The method used consisted of 4 parts, that was Cropping, Filtering, Detecting Edge, and Sharpening Edge. The results of the research showed that the method could clarify edge detection of the objects contained in the image, so that the infiltrate could be more easily recognized. With this easiness, it will help the doctor to remove doubts for infiltrate observations in the Infant's lungs

Computer-aided diagnosis in chest radiography

Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an automatic system for detecting regions with abnormal texture, that is applied to a database of images from a tuberculosis screening program

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Semiautomatic Detection of Scoliotic Rib Borders From Posteroanterior Chest Radiographs

3-D assessment of scoliotic deformities relies on an accurate 3-D reconstruction of bone structures from biplanar X-rays, which requires a precise detection and matching of anatomical structures in both views. In this paper, we propose a novel semiautomated technique for detecting complete scoliotic rib borders from PA-0° and PA-20° chest radiographs, by using an edge-following approach with multiple-path branching and oriented filtering. Edge-following processes are initiated from user starting points along upper and lower rib edges and the final rib border is obtained by finding the most parallel pair among detected edges. The method is based on a perceptual analysis leading to the assumption that no matter how bent a scoliotic rib is, it will always present relatively parallel upper and lower edges. The proposed method was tested on 44 chest radiographs of scoliotic patients and was validated by comparing pixels from all detected rib borders against their reference locations taken from the associated manually delineated rib borders. The overall 2-D detection accuracy was 2.64 ± 1.21 pixels. Comparing this accuracy level to reported results in the literature shows that the proposed method is very well suited for precisely detecting borders of scoliotic ribs from PA-0° and PA-20° chest radiographs.CIHR / IRS

Anatomy X-Net: A Semi-Supervised Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification

Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. This work proposes an anatomy-aware attention-based architecture named Anatomy X-Net, that prioritizes the spatial features guided by the pre-identified anatomy regions. We leverage a semi-supervised learning method using the JSRT dataset containing organ-level annotation to obtain the anatomical segmentation masks (for lungs and heart) for the NIH and CheXpert datasets. The proposed Anatomy X-Net uses the pre-trained DenseNet-121 as the backbone network with two corresponding structured modules, the Anatomy Aware Attention (AAA) and Probabilistic Weighted Average Pooling (PWAP), in a cohesive framework for anatomical attention learning. Our proposed method sets new state-of-the-art performance on the official NIH test set with an AUC score of 0.8439, proving the efficacy of utilizing the anatomy segmentation knowledge to improve the thoracic disease classification. Furthermore, the Anatomy X-Net yields an averaged AUC of 0.9020 on the Stanford CheXpert dataset, improving on existing methods that demonstrate the generalizability of the proposed framework

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice