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

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

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte

The role of deep learning in structural and functional lung imaging

Background: Structural and functional lung imaging are critical components of pulmonary patient care. Image analysis methods, such as image segmentation, applied to structural and functional lung images, have significant benefits for patients with lung pathologies, including the computation of clinical biomarkers. Traditionally, machine learning (ML) approaches, such as clustering, and computational modelling techniques, such as CT-ventilation imaging, have been used for segmentation and synthesis, respectively. Deep learning (DL) has shown promise in medical image analysis tasks, often outperforming alternative methods. Purpose: To address the hypothesis that DL can outperform conventional ML and classical image analysis methods for the segmentation and synthesis of structural and functional lung imaging via: i. development and comparison of 3D convolutional neural networks (CNNs) for the segmentation of ventilated lung using hyperpolarised (HP) gas MRI. ii. development of a generalisable, multi-centre CNN for segmentation of the lung cavity using 1H-MRI. iii. the proposal of a framework for estimating the lung cavity in the spatial domain of HP gas MRI. iv. development of a workflow to synthesise HP gas MRI from multi-inflation, non-contrast CT. v. the proposal of a framework for the synthesis of fully-volumetric HP gas MRI ventilation from a large, diverse dataset of non-contrast, multi-inflation 1H-MRI scans. Methods: i. A 3D CNN-based method for the segmentation of ventilated lung using HP gas MRI was developed and CNN parameters, such as architecture, loss function and pre-processing were optimised. ii. A 3D CNN trained on a multi-acquisition dataset and validated on data from external centres was compared with a 2D alternative for the segmentation of the lung cavity using 1H-MRI. iii. A dual-channel, multi-modal segmentation framework was compared to single-channel approaches for estimation of the lung cavity in the domain of HP gas MRI. iv. A hybrid data-driven and model-based approach for the synthesis of HP gas MRI ventilation from CT was compared to approaches utilising DL or computational modelling alone. v. A physics-constrained, multi-channel framework for the synthesis of fully-volumetric ventilation surrogates from 1H-MRI was validated using five-fold cross-validation and an external test data set. Results: i. The 3D CNN, developed via parameterisation experiments, accurately segmented ventilation scans and outperformed conventional ML methods. ii. The 3D CNN produced more accurate segmentations than its 2D analogues for the segmentation of the lung cavity, exhibiting minimal variation in performance between centres, vendors and acquisitions. iii. Dual-channel, multi-modal approaches generate significant improvements compared to methods which use a single imaging modality for the estimation of the lung cavity. iv. The hybrid approach produced synthetic ventilation scans which correlate with HP gas MRI. v. The physics-constrained, 3D multi-channel synthesis framework outperformed approaches which did not integrate computational modelling, demonstrating generalisability to external data. Conclusion: DL approaches demonstrate the ability to segment and synthesise lung MRI across a range of modalities and pulmonary pathologies. These methods outperform computational modelling and classical ML approaches, reducing the time required to adequately edit segmentations and improving the modelling of synthetic ventilation, which may facilitate the clinical translation of DL in structural and functional lung imaging

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Computed tomography image analysis for the detection of obstructive lung diseases

Damage to the small airways resulting from direct lung injury or associated with many systemic disorders is not easy to identify. Non-invasive techniques such as chest radiography or conventional tests of lung function often cannot reveal the pathology. On Computed Tomography (CT) images, the signs suggesting the presence of obstructive airways disease are subtle, and inter- and intra-observer variability can be considerable. The goal of this research was to implement a system for the automated analysis of CT data of the lungs. Its function is to help clinicians establish a confident assessment of specific obstructive airways diseases and increase the precision of investigation of structure/function relationships. To help resolve the ambiguities of the CT scans, the main objectives of our system were to provide a functional description of the raster images, extract semi-quantitative measurements of the extent of obstructive airways disease and propose a clinical diagnosis aid using a priori knowledge of CT image features of the diseased lungs. The diagnostic process presented in this thesis involves the extraction and analysis of multiple findings. Several novel low-level computer vision feature extractors and image processing algorithms were developed for extracting the extent of the hypo-attenuated areas, textural characterisation of the lung parenchyma, and morphological description of the bronchi. The fusion of the results of these extractors was achieved with a probabilistic network combining a priori knowledge of lung pathology. Creating a CT lung phantom allowed for the initial validation of the proposed methods. Performance of the techniques was then assessed with clinical trials involving other diagnostic tests and expert chest radiologists. The results of the proposed system for diagnostic decision-support demonstrated the feasibility and importance of information fusion in medical image interpretation.Open acces

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients

The significant clinical heterogeneity of Multiple Myeloma (MM) patients implies that a set of consolidated biomarkers is currently missing. Radiomics is an advanced, quantitative feature-based methodology for image analysis. We assess the feasibility of an AI-based approach for the automatic stratification of MM patients from CT data, and for the automatic identification of radiological biomarkers with a possible prognostic value. A retrospective analysis of n = 33 transplanted MM with focal lesion were performed via an open-source toolbox that extracted 109 radiomics features. The redundancy reduction was realized via correlation and principal component analysis. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. The Mann\u2013 Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This computational approach to the interpretation of radiomics features shows the potential for the stratification of relapsed and non-relapsed MM patients, and could represent a prognostic image-based procedure for determining the disease follow-up and therapy