Genauigkeit der computergestützten Detektion von pulmonalen Läsionen in der Thoraxröntgenaufnahme

Das Röntgenbild stellt eine ubiquitäre diagnostische Methode von thorakalen Läsionen dar. Die pulmonale Läsion ist ein häufiger radiographischer Befund. Die Detektion von kleinen Läsionen innerhalb von komplexen parenchymalen Strukturen ist eine tägliche klinische Herausforderung. Die Unterstützung der Diagnose durch Computer Aided Detection (CAD) Systeme visiert die Optimierung der Sensitivität und des Zeitmanagements im radiologischen Alltag an. In der vorliegenden Studie haben wir die Effizienz der computergestützten Diagnostik (CAD) des Softwarepakets SoftView 2.4A für Bone-Suppressed Images (BSI) und OnGuard 5.2 (Riverain Technologies, Miamisburg, OH, USA) in der automatisierten Detektion pulmonaler Läsionen in den Röntgenaufnahmen untersucht. Zu diesem Zweck haben wir retrospektiv 100 konventionelle posteroanteriore Röntgenaufnahmen von 100 Patienten (64 Männer und 36 Frauen; Durchschnittsalter von 67 Jahren mit einer Spanne 35-93 Jahre) mit histologisch verifizierten Lungenrundherden (75% maligne, 25% benigne) analysiert. Berücksichtigt wurden pulmonale Läsionen von 5 bis 85 mm Durchmesser. Im Fall des Vorliegens mehrerer pulmonaler Läsionen wurde nur die größte davon in die Studie eingeschlossen. Alle in den Thoraxröntgenaufnahmen identifizierten pulmonalen Läsionen wurden zunächst durch eine innerhalb von 3 Monaten erfolgte Computertomographie der Lunge zum Ausschluss von Summationseffekten sowie durch bioptische Kontrolle verifiziert. Das Programm SoftView detektiert Strukturen, die anhand ihrer Dichte und Morphologie mit den Rippen oder den Claviculae vereinbar sind. Diese Strukturen werden in einem zweiten Schritt automatisch supprimiert (vom Bild subtrahiert). Durch diese Subtraktion des geschätzten Knochenbildes entsteht ein reines Weichteilbild, das BSI-Bild. Das Programm OnGuard markiert mit einem Kreis die Regionen in den BSI-Bildern, die mit einem pulmonalen Rundherd vereinbar sind. Zur Bewertung des CAD-Systems wir beziehen uns auf die folgenden Fragen: 1. Ob BSI das objektive Signal-Rauschleistung Verhältnis verbessert 2. Sensitivität und Spezifität des Systems. Das objektive Signal-Rauschleistung-Verhältnis, bezeichnet als Läsions-Hintergrund-Kontrast wird durch das BSI SoftView nicht verändert; P = 0,735, n = 100, Wilcoxon Rank-sum Test. Die Bildverbesserung durch BSI ist am ehesten auf die durch die Mustervereinfachung verbesserte subjektive Wahrnehmung zurückzuführen. OnGuard weist eine Sensitivität von 62% und eine Spezifität von 58% bezüglich der Detektion von nodulären Läsionen in Thoraxröntgenaufnahmen auf. 20% der richtig positiven Fälle (true positive, TP) wurden histologisch als benigne und 80% als maligne verifiziert, 47% der übersehenen (falsch negativen, FN) Läsionen waren als benigne und 53% als maligne klassifiziert. Die Detektionsrate maligner Läsionen überstieg somit die Detektionsrate benigner Läsionen; P = 0,012, n = 100, Chi-Quadrat-Test. CAD eignet sich anhand unserer Ergebnisse nicht für als Differenzierungsoption bei pulmonalen Rundherden und sollte eher als reines Detektionstool weiterentwickelt werden. Es empfiehlt sich die Anwendung von CAD mit einer kritischen visuellen radiologischen Auswertung zu kombinieren (Second-Reading mode). Fortführende prospektive Studien, die nicht nur die Effektivität sondern auch das Qualitäts-Zeit-Kosten-Profil der Echtzeit CAD-Implementierung wären für die zukünftige Bewertung der CAD-Systeme benötigt

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Implementation and evaluation of a bony structure suppression software tool for chest X-ray imaging

Includes abstract.Includes bibliographical references.This project proposed to implement a bony structure suppression tool and analyse its effects on a texture-based classification algorithm in order to assist in the analysis of chest X-ray images. The diagnosis of pulmonary tuberculosis (TB) often includes the evaluation of chest X-ray images, and the reliability of image interpretation depends upon the experience of the radiologist. Computer-aided diagnosis (CAD) may be used to increase the accuracy of diagnosis. Overlapping structures in chest X-ray images hinder the ability of lung texture analysis for CAD to detect abnormalities. This dissertation examines whether the performance of texturebased CAD tools may be improved by the suppression of bony structures, particularly of the ribs, in the chest region

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

Relative Merits of 3D Visualization for the Detection of Subtle Lung Nodules

A new imaging modality called bi-plane correlation imaging (BCI) was examined to determine the merits of using BCI with stereoscopic visualization to detect subtle lung nodules. In the first aim of this project, the optimal geometry for conventional projection imaging applications was assessed using a theoretical model to develop generic results for MTF, NNPS, eDQE. The theoretical model was tested with a clinical system using two magnifications and two anthropomorphic chest phantoms to assess the modalities of single view CXR and stereo/BCI. Results indicated that magnification can potentially improve the signal and noise performance of digital images. Results also demonstrated that a cross over point occurs in the spatial frequency above and below which the effects of magnification differ indicating that there are task dependent tradeoffs associated with magnification. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by 30-40%. For both anthropomorphic phantoms, at iso-dose, eDQE(0) for stereo/BCI was ~100 times higher than that for CXR. Magnification at iso-dose improved eDQE(0) by ~10 times for BCI. Increasing the dose did not improve results. The findings indicated that stereo/BCI with magnification may improve detection of subtle lung nodules compared to single view CXR. With quantitative results in place, a pilot clinical trial was constructed. Human subject data was acquired with a BCI acquisition system. Subjects were imaged in the PA position as well as two oblique angles. Realistic simulated lesions were added to a subset of subjects determined to be nodule free. A BCI CAD algorithm was also applied. In randomized readings, radiologists read the cases according to viewing protocol. For the radiologist trainees, the AUC of lesion detection was seen to improve by 2.8% (p < 0.05) for stereoscopic viewing after monoscopic viewing compared to monoscopic viewing only. A 13% decrease in false positives was observed. Stereo/BCI as an adjunct modality was beneficial. However, the full potential of stereo/BCI as a replacement modality for single view chest x-ray may be realized with improved observer training, clinically relevant stereoscopic displays, and more challenging detection tasks.Doctor of Philosoph

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd