Towards greater clarity for the analysis of imaging studies: Development & validation of an alternative to the area under the receiver-operator characteristic curve.

This thesis arose from a 2006 study performed by the author and his collaborators that attempted to gain regulatory approval for computer-assisted detection (CAD) software. The USA Food & Drug Administration (FDA) obliged us to use the change in the area under the receiver-operator characteristic curve (ROC AUC) as our primary outcome. Despite its wide dissemination in radiology research, we found implementation of ROC AUC very problematic. This thesis explores the hurdles we encountered and argues for an alternative approach. Chapter 1 describes the rationale for and against ROC AUC as a measure of diagnostic performance. An alternative analysis based on net benefit is proposed on the basis that it is more transparent and simpler to interpret. Chapter 2 uses the net benefit method to analyse a multi-reader multi-case (MRMC) study of CAD for CT colonography. The analysis requires an estimate of relative misclassification costs for false-negative versus false-positive diagnoses; “W”. This study used a conservative value for W, arrived at via consensus. In Chapter 3 an evidence-based value for W in the context of screening for colorectal cancer and polyps by CT colonography is arrived at via a discrete choice experiment (DCE) of patients and healthcare workers. Chapter 4 uses the value for W obtained in Chapter 3 in a net benefit analysis to compare observer performance in two MRMC studies of CAD for CT colonography. Chapter 5 obtains W by DCE for a different clinical context – detection of extracolonic pathology by CT colonography. Chapter 6 describes a systematic review that aims to determine whether reporting of MRMC ROC AUC methods in the radiological literature is comprehensive. Chapter 7 then provides guidelines for the comprehensive reporting of MRMC ROC AUC studies. The thesis finishes with a summary of the work performed and suggestions for further research

Multi-scale and multi-spectral shape analysis: from 2d to 3d

Shape analysis is a fundamental aspect of many problems in computer graphics and computer vision, including shape matching, shape registration, object recognition and classification. Since the SIFT achieves excellent matching results in 2D image domain, it inspires us to convert the 3D shape analysis to 2D image analysis using geometric maps. However, the major disadvantage of geometric maps is that it introduces inevitable, large distortions when mapping large, complex and topologically complicated surfaces to a canonical domain. It is demanded for the researchers to construct the scale space directly on the 3D shape. To address these research issues, in this dissertation, in order to find the multiscale processing for the 3D shape, we start with shape vector image diffusion framework using the geometric mapping. Subsequently, we investigate the shape spectrum field by introducing the implementation and application of Laplacian shape spectrum. In order to construct the scale space on 3D shape directly, we present a novel idea to solve the diffusion equation using the manifold harmonics in the spectral point of view. Not only confined on the mesh, by using the point-based manifold harmonics, we rigorously derive our solution from the diffusion equation which is the essential of the scale space processing on the manifold. Built upon the point-based manifold harmonics transform, we generalize the diffusion function directly on the point clouds to create the scale space. In virtue of the multiscale structure from the scale space, we can detect the feature points and construct the descriptor based on the local neighborhood. As a result, multiscale shape analysis directly on the 3D shape can be achieved

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Detection and staging of colonic lesions using computed tomography and magnetic resonance imaging

In Sweden, more than 6000 new patients were diagnosed with colorectal cancer in 2015 of which over 4000 patients had colon cancer and 1800 died from the disease. It is the second most common cancer after breast- and prostate cancer. In the last decade, significant improvement in the treatment of both rectal and colon cancer have been achieved. Diagnostic imaging, using CT, MRI and PET/CT, has become essential in the preoperative work-up. New neoadjuvant treatment strategies are under study in colon cancer. In the selection of patients for these treatments, pre- and post-treatment imaging has also become of interest. The overall aim of this thesis is to evaluate cross sectional imaging modalities for detection of colonic polyps and staging of patients with colon cancer using CT and MRI. The aim of paper 1 was to investigate the impact of radiation dose and spatial resolution in detecting colonic polyps in a phantom study simulating computed tomographic colonography (CTC). By using different scanning protocols with different slice -thickness, pitch and tube current we showed that the dose level could be substantially reduced by lowering the tube current without compromising the detection rate for polyps larger than 5 mm. The aim of paper 2 was to evaluate if high resolution MRI of colon cancer contributed to the standard staging procedure with CT with respect to assessment of local tumour extent, nodal staging and extramural venous invasion (EMVI). An advantage of MRI over CT due to its soft tissue discrimination to identify prognostic factors such as tumour stage and extramural venous invasion was found. The result of nodal staging for both modalities were equally moderate. The aim of paper 3 was to evaluate commonly used imaging CT criteria for lymph node metastases in predicting stage III disease. Of the different imaging criteria, morphological features performed best specifically internal heterogeneity and irregular outer border. None of the size criteria were predictive. The aim of paper 4 was to validate morphological CT criteria from paper 3 in a prospectively collected patient cohort using two observers. By using the criteria internal heterogeneity and a combination including irregular outer border, a moderate sensitivity and high specificity was achieved predicting stage III disease. CT and high resolution MRI can be used to classify colonic tumours into not locally advanced or locally advanced. The prediction of lymph node metastases with the most commonly used image modalities is however unsettled and challenged. In the setting of selecting patients to neoadjuvant chemotherapy, the ongoing trials so far have used inclusion criteria based on tumour T-stage only (T3cd-T4 as locally advanced). Patients with lower tumour T-stage but still have other adverse prognostic feature such as regional metastases will therefore potentially be undertreated and patients with no metastases will potentially be overtreated. Search for other prognostic factors identified on cross sectional imaging has to be performed

Feature extraction to aid disease detection and assessment of disease progression in CT and MR colonography

Computed tomographic colonography (CTC) is a technique employed to examine the whole colon for cancers and premalignant adenomas (polyps). Oral preparation is taken to fully cleanse the colon, and gas insufflation maximises the attenuation contrast between the enoluminal colon surface and the lumen. The procedure is performed routinely with the patient both prone and supine to redistribute gas and residue. This helps to differentiate fixed colonic pathology from mobile faecal residue and also helps discover pathology occluded by retained fluid or luminal collapse. Matching corresponding endoluminal surface locations with the patient in the prone and supine positions is therefore an essential aspect of interpretation by radiologists; however, interpretation can be difficult and time consuming due to the considerable colonic deformations that occur during repositioning. Hence, a method for automated registration has the potential to improve efficiency and diagnostic accuracy. I propose a novel method to establish correspondence between prone and supine CT colonography acquisitions automatically. The problem is first simplified by detecting haustral folds which are elongated ridgelike endoluminal structures and can be identified by curvature based measurements. These are subsequently matched using appearance based features, and their relative geometric relationships. It is shown that these matches can be used to find correspondence along the full length of the colon, but may also be used in conjunction with other registration methods to achieve a more robust and accurate result, explicitly addressing the problem of colonic collapse. The potential clinical value of this method has been assessed in an external clinical validation, and the application to follow-up CTC surveillance has been investigated. MRI has recently been applied as a tool to quantitatively evaluate the therapeutic response to therapy in patients with Crohn's disease, and is the preferred choice for repeated imaging. A primary biomarker for this evaluation is the measurement of variations of bowel wall thickness on changing from the active phase of the disease to remission; however, a poor level of interobserver agreement of measured thickness is reported and therefore a system for accurate, robust and reproducible measurements is desirable. I propose a novel method which will automatically track sections of colon, by estimating the positions of elliptical cross sections. Subsequently, estimation of the positions of the inner and outer bowel walls are made based on image gradient information and therefore a thickness measurement value can be extracted

Strategies for Colorectal Cancer Screening

The incidence of colorectal cancer (CRC) is increasing worldwide. CRC has high mortality when detected at advanced stages, yet it is also highly preventable. Given the difficulties in implementing major lifestyle changes or widespread primary prevention strategies to decrease CRC risk, screening is the most powerful public health tool to reduce mortality. Screening methods are effective but have limitations. Furthermore, many screen-eligible persons remain unscreened. We discuss established and emerging screening methods, and potential strategies to address current limitations in CRC screening. A quantum step in CRC prevention might come with the development of new screening strategies, but great gains can be made by deploying the available CRC screening modalities in ways that optimize outcomes while making judicious use of resources

Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies

The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes