A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease

Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution. In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data. To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD. The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs. The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms. In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus

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

Segmentation of 3D image data using advanced textural and shape features

Tato diplomová práce se zabývá nejprve teoretickým popisem řady metod texturní a tvarové analýzy. Tyto metody jsou v několika publikovaných článcích využity pro automatickou detekci lézí v páteři na CT snímcích, v této práci jsou některé z těchto článků krátce prezentovány. Dále diplomová práce obsahuje popis různých klasifikátorů, které se využívají pro klasifikaci příznakových vektorů odvozených uvedenými metodami. Realizační částí práce je návrh a implementace vlastního řešení segmentace obrazových dat (metastatických lézí v obratlích) s využitím klasifikace příznakových vektorů tvořených texturními a tvarovými příznaky. Práce se také věnuje výběru významných příznaků pro segmentaci. Segmentační algoritmus je testován na medicínských datech.This thesis first describes theory of range of methods of textural and shape analysis. In several published articles some of the mentioned methods are used for automatic detection of lesion in spine in CT images. Some of these articles are shortly presented (in this thesis). Next part of the thesis includes description of various classifiers which are used for classification of feature vectors. Practical part of the thesis is a design and implementation of image data segmentation solution (metastatic lesions in vertebrae) with use of classification of feature vectors formed by texture and shape symptoms. The thesis also deals with the selection of significant features for segmentation. Segmentation algorithm is tested on medical data.

Radiographic Assessment of Hip Disease in Children with Cerebral Palsy: Development of a Core Measurement Set and Analysis of an Artificial Intelligence System

Cerebral palsy is the most common physical disability during childhood. Cerebral palsy related hip disease is caused by an imbalance of muscle forces, resulting in progressive migration of the hip to complete dislocation. This can decrease function and quality of life. The prevention of hip dislocation is possible if detected early. Therefore, surveillance programmes have been set up to monitor children with cerebral palsy enabling clinicians to intervene early and improve outcomes. Currently, hip disease is assessed by analysing pelvic radiographs with various geometric measurements. This time-consuming task is undertaken frequently when monitoring a child with cerebral palsy. This thesis aimed to identify the key radiographic parameters used by clinicians (the core measurement set), and then build an artificial intelligence system to automate the calculation of this core measurement set. A systematic review was conducted identifying a comprehensive list of previously reported measurements from studies measuring radiographic outcomes in cerebral palsy children with hip pathologies. Fifteen measurements were identified from the systematic review, of which Reimers’ migration percentage was the most commonly reported. These measurements were used to perform a two-round Delphi study among orthopaedic surgeons and physiotherapists. Participants rated the importance of each measurement using a nine-point Likert scale (‘not important’ to critically important’). After the two rounds of the Delphi process, Reimers’ migration percentage was included in the core measurement set. Following the final consensus meeting, the femoral head-shaft angle was also included. The anteroposterior pelvic radiographs of 1650 children were then used to build an artificial intelligence system integrating the core measurement set, in collaboration with engineers from the University of Manchester. The newly developed artificial intelligence system was assessed by comparing its ability to calculate measurements and outline the pelvis and femur on a radiograph. The reliability of the dataset used to train the model was also analysed. The proposed artificial intelligence model achieved a ‘good to excellent’ inter-observer reliability across 450 radiographs when comparing its ability to calculate Reimers’ migration percentage to five clinicians. Its ability to outline the pelvis and proximal femur was ‘adequate’ with the better performance observed in the pelvis than the femur. The reliability of the training dataset used to teach the artificial intelligence model was ‘good’ to ‘very good’. Artificial intelligence systems are feasible solutions to optimise the efficiency of hip radiograph analysis in cerebral palsy. Studies are warranted to include the core measurement set as a minimum when reporting on hip disease in cerebral palsy. Future research should investigate the feasibility of implementing a risk score to predict the likelihood of hip displacement

Development and assessment of Computer Aided Detection (CAD) software for assisting diagnosis in cervical spine projection radiography

Introduction Cervical spine injuries are a major burden on hospital services and have serious consequences for morbidity and mortality; this also affects society due to the associated high care and medical costs. These injuries have the potential to be missed or misdiagnosed, although it must be stated that this phenomena is not unique to cervical spine injuries, and has been seen throughout most imaging services. One possible method to counter this is the use of computer aided detection (CAD) software integrated into the imaging process. This can help increase sensitivity and specificity scores (and thus area under a curve (AUC) scores) by indicating any injuries/pathologies using a pattern recognition algorithm. Methods Lateral cervical spine images were collected from clinical cases and anonymysed by the hospital. These were segmented using a Matlab script to develop ground truth images for the computer scientists to develop cervical spine CAD (CSPINE-CAD) software using machine learning algorithms. The CSPINE CAD software was then assessed in a number of studies as described below. Participants were a convenience sample recruited at the University of Exeter and the Royal Devon and Exeter hospital, and were involved in three tests. These tests all investigated the AUC differences when making a diagnosis without, and with the CSPINE-CAD software. These three tests were: The first test involving five third year radiography students each diagnosing the same five lateral C-spine radiographs, first without and then with the use of the CSPINE-CAD software. Answers were provided by the students via a comments box in which they would make an original diagnosis, then apply the CAD software and then make a re-diagnosis. Upon completion a questionnaire was filled in about their opinions, feedback and confidence whilst using the software. The second test involved 11 third year radiography students from the same cohort each diagnosing 30 lateral C-spine radiographs. This involved using a representation of the CSPINE-CAD software, and followed the same method of diagnosis (a comments box) as in the first test, concluding with a questionnaire. The third test involved 26 participants made up of junior doctors and qualified radiographers, each diagnosing 30 radiographs without and with CSPINE-CAD. This third test did not utilise a comments box, but instead used an answer sheet which contained blank boxes representing each vertebral body and each vertebral junction. These boxes were filled in by the participant using a number between one and six (one representing no injury, and six being 80-100% confident there is an injury). These boxes would all be filled for each image twice; once without CAD and once with CAD. The next image was loaded and the process repeated. Upon completion a questionnaire was again provided to allow the participants to give feedback and confidence about the software. Due to the ambiguity in the language used in the comments boxes of the first and second tests, it was concluded to analyse and produce two results per test. The first analysis was a benefit of the doubt analysis in which the diagnosis provided by the participants would receive some latitude (e.g. misalignment of C5 would be accepted if the “true” answer was misalignment C5/C6). The second analysis was more verbatim and received no latitude. All three tests were compared against the gold standard of a radiologists report, and calculated for AUC scores without and with CSPINE-CAD. Results None of the three test results were statistically significant. The first test showed an AUC increase of 1.39% (with latitude) and 9.54% (no latitude) when using CAD. The second test showed an AUC increase of 1.64% (with latitude) and a loss of 0.25% (no latitude) when using CAD. The third test showed that across all confidence values (2-6) the AUC is higher 1.65% without CAD. Additionally when reviewing only the highest confidence value (6) the AUC increases with CAD by 0.66%. Questionnaire data showed an increase in average confidence when using CAD across all three tests by 12%, 20% and 9.24% respectively, with the majority of participants agreeing that CAD was helpful as a second “pair of eyes” with scores of 100%, 100% and 73%. Conclusion Due to sample sizes and the amount of images being small a statistical significant result could not be reached. Although CSPINE-CAD has shown to be a possible method to reduce missed or misdiagnosed cervical spine injuries, further investigation and development is needed into this CAD software

Image Quality Assessment for Population Cardiac MRI: From Detection to Synthesis

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Left Ventricular (LV) cardiac anatomy and function are widely used for diagnosis and monitoring disease progression in cardiology and to assess the patient's response to cardiac surgery and interventional procedures. For population imaging studies, CMR is arguably the most comprehensive imaging modality for non-invasive and non-ionising imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Due to insufficient radiographer's experience in planning a scan, natural cardiac muscle contraction, breathing motion, and imperfect triggering, CMR can display incomplete LV coverage, which hampers quantitative LV characterization and diagnostic accuracy. To tackle this limitation and enhance the accuracy and robustness of the automated cardiac volume and functional assessment, this thesis focuses on the development and application of state-of-the-art deep learning (DL) techniques in cardiac imaging. Specifically, we propose new image feature representation types that are learnt with DL models and aimed at highlighting the CMR image quality cross-dataset. These representations are also intended to estimate the CMR image quality for better interpretation and analysis. Moreover, we investigate how quantitative analysis can benefit when these learnt image representations are used in image synthesis. Specifically, a 3D fisher discriminative representation is introduced to identify CMR image quality in the UK Biobank cardiac data. Additionally, a novel adversarial learning (AL) framework is introduced for the cross-dataset CMR image quality assessment and we show that the common representations learnt by AL can be useful and informative for cross-dataset CMR image analysis. Moreover, we utilize the dataset invariance (DI) representations for CMR volumes interpolation by introducing a novel generative adversarial nets (GANs) based image synthesis framework, which enhance the CMR image quality cross-dataset

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis