An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin

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

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv

Automated analysis of 3D echocardiography

In this thesis we aim at automating the analysis of 3D echocardiography, mainly targeting the functional analysis of the left ventricle. Manual analysis of these data is cumbersome, time-consuming and is associated with inter-observer and inter-institutional variability. Methods for reconstruction of 3D echocardiographic images from fast rotating ultrasound transducers is presented and methods for analysis of 3D echocardiography in general, using tracking, detection and model-based segmentation techniques to ultimately fully automatically segment the left ventricle for functional analysis. We show that reliable quantification of left ventricular volume and mitral valve displacement can be achieved using the presented techniques.SenterNovem (IOP Beeldverwerking, grant IBVC02003), Dutch Technology Foundation STW (grant 06666)UBL - phd migration 201

Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology

Liver surgical resection is one of the most frequently used curative therapies. However, resectability is problematic. There is a need for a computer-assisted surgical planning and simulation system which can accurately and efficiently simulate the liver, vessels and tumours in actual patients. The present project describes the development of these core segmentation and deformable modelling techniques. For precise detection of irregularly shaped areas with indistinct boundaries, the segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets. To improve efficiency, a chessboard distance transform was used to replace part of the GVF effort. To automatically initialize the liver volume detection process, a rotating template was introduced to locate the starting slice. For shape maintenance during the segmentation process, a simplified object shape learning step was introduced to avoid occasional significant errors. Skeletonization with fuzzy connectedness was used for vessel segmentation. To achieve real-time interactivity, the deformation regime of this system was based on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh refinement to raise the deformation accuracy and the mesh control quality. This method was now extended to a multiple soft-tissue constraint system, by supplementing it with an adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide comparison of classic measures. Adjustable feature and parameter settings were thus provided, to make tissues of interest distinct from adjacent structures, keeping the mesh suitable for on-line topological transformation and deformation. More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver datasets were tested to evaluate the performance of the segmentation method. Instrument manipulations of probing, grasping, and simple cutting were successfully simulated on deformable constraint liver tissue models. This project was implemented in conjunction with the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was judged satisfactory by the consultant hepatic surgeon

Automated Analysis of 3D Stress Echocardiography

__Abstract__ The human circulatory system consists of the heart, blood, arteries, veins and capillaries. The heart is the muscular organ which pumps the blood through the human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium into the right ventricle, which pumps the blood into the pulmonary arteries. The blood is carried to the lungs, where it passes through a capillary network that enables the release of carbon dioxide and the uptake of oxygen. Oxygenated blood then returns to the heart via the pulmonary veins and flows from the left atrium into the left ventricle. The left ventricle then pumps the blood through the aorta, the major artery which supplies blood to the rest of the body [Drake et a!., 2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system remains healthy. Disease of the cardiovascular system, if untreated, ultimately leads to the failure of other organs and death