Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

Using active contours for automated tracking of UV and EUV solar flare ribbons

Solar flare UV and EUV images show elongated bright ``ribbons'' that move over time. If these ribbons are assumed to locate the footpoints of magnetic field lines reconnecting in the corona then it is clear that studying their evolution can provide an important insight into the reconnection process. We propose an image processing method based on active contours (commonly referred to as ``snakes''), for tracking UV and EUV flare ribbons in images from the transition region and coronal explorer (TRACE). Our method aims to provide an efficient, accurate and automatic tool to aid in the study of flare ribbon evolution in large datasets. Chapter 1 provides an introduction to the Sun and solar activity, with a more focussed section on solar flares where the mechanism for the creation of flare ribbons is discussed. We also outline the motivation for solar physics research as a whole and more specifically the motivations behind this project. In this chapter we introduce the TRACE satellite as the source the images used in this project, with a summary of its hardware and the UV and EUV channels which the images used in this project are captured in. Chapter 2 introduces some basics of image processing, such as applying spatial filters. We also look at the different approaches to image segmentation including a more in depth study of active contours. The role of image processing in solar physics and the driving forces for image processing development in solar physics are summarised. The final section of this chapter presents a review of previous methods used for the tracking of solar flare ribbons, including manual, semi-automatic and fully automatic methods. The first part of Chapter 3 details the pre-processing steps applied to the TRACE data before its use by our algorithm. The second part of this chapter introduces our algorithm, with a general overview and detailed discussion of the constituent parts. We also show results from initial tests carried out using a simulated test image, and demonstrate how different parameters of the algorithm can affect its result. Chapter 4 shows results obtained from using our algorithm on TRACE flare images. Some modifications to the algorithm were deemed necessary after applying it to only a small number of flare images, the initial part of this chapter covers the reasons for the modifications and the modifications themselves. The remainder of the chapter presents results of the algorithm applied to a number image sequences from different flares. The results are presented and discussed for each flare separately, with one flare being used as an example of how the parameters of the algorithm can be adapted to suit different flares and images. Chapter 5 discusses to what extent the aim of the project has been achieved and presents a summary of the problems encountered in applying our algorithm to flare images. This chapter finishes with a look at some ideas for future work, both for our algorithm specifically, and for general efforts at flare ribbon tracking

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Automatic Active Contour Modelling and Its Potential Application for Non-Destructive Testing

Active contouring techniques are very useful in medical imaging, digital mapping, non-destructive ultrasonic evaluation, etc. Therefore, we try to explore and investigate the advanced automatic active contouring methods, which can benefit the aforementioned applications. In this thesis work, we study the automatic active contour models adopted for the object characterization, whose extensions could include the potential defect analysis in the ultrasonic non-destructive testing. The active contouring scheme (also called a snake) or an energy minimizing spline, is an algorithm which is very sensitive to the manually marked initial points, and thereby requires an expertly operation. Therefore, we make new research endeavors to handle this major problem and design a new expert-free snake technique, which can lead to the completely automatic contouring technology for the future applications. Even though this initialization problem has been addressed in the literature for quite a while, to the best of our knowledge, there exists no satisfactory solution so far. In this thesis, we propose a novel initialization algorithm for the automatic snake technique, which can possess a faster convergence than other existing automatic contouring methods and also avoid the human operational error incurred in the conventional snake schemes

Dynamical models and machine learning for supervised segmentation

This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials