Segmentation of Medical Images, Applications in Echocardiography and Nuclear Medicine

Segmentation is an important task in all kinds of image analysis. In medical image analysis segmentation has a great clinical value since the aim could be to localize organs or pathologies in order to raise the quality of diagnoses. This thesis consists of three papers where different segmentation techniques have been applied to different imaging modalities. In the first paper a full computer assisted diagnosis system is presented where the aim is to find abnormal lesions in scintigraphy images of the kidneys. Here we have two segmentation parts; first segmentation of each kidney in the images using an active shape model and then localization of potential lesions using thresholding. In a test group of 56 patients the segmentations work very well just like the classification of lesions does. The second application is segmentation of the left ventricle in echocardiographic images. This segmentation is important when measuring the left ventricular function. The segmentation is done using a region based snake where the data term is driven by virtual image forces derived from the image intensities. To overcome problems with the cardiac valve opening and closing during the cardiac cycle, we annotate two anchor points, one on each side of the valve. We track these through the cycle in order to minimize user interaction and no segmentation is done over the valve. This method shows promising results. In the third paper we have developed a measure of the shape of the septum, the wall between the left and the right ventricle, to be used in echocardiographic images of patients with a mechanical pump attached to their heart as a bridge to transplantation. Such a measure can be useful when tuning the speed of the pump. Here the segmentation of the septum is achieved using the shortest path algorithm. The septum measure is then a measure of how much it deviates from a straight septum. Our septum measure corresponds in most cases to the assessments from a physician. These three applications show the usefulness of segmentation in a variety of applications within medical imaging

Analysis of Medical Images : Registration, Segmentation and Classification

A large number of medical examinations involve images in some way. Images can be used for diagnostics, follow-up studies and treatment planning. In this thesis mathematical methods have been developed and adapted in order to analyze medical images. Several applications for different imaging modalities have been studied and the usefulness of such methods is demonstrated.A complete system for detection and diagnosis of kidney lesions in scintigraphy images has been developed. We segment the kidneys with the use of an active shape model. The uptake of a biological molecule is then compared to the uptake in a healthy kidney and potential lesions are detected. A number of properties of the potential lesions are gathered and the lesions are classified as healthy or unhealthy with a linear classifier. We are able to correctly classify 86 % of the lesions.Ultrasound images have also been studied. In the first case for the purpose of segmenting the left heart ventricle, which can be used for computing the ejection fraction. This was done using a region based snake with anchor points at each side of the cardiac valve. The second application in ultrasound images is also of the heart but with patients that, due to heart failure, have had a mechanical pump implanted. The septum wall between the ventricles is segmented using a shortest path approach and a measure of how much the septum bulges towards either of the ventricles is obtained. By studying this measure a more objective indication is given on whether the speed of the pump is correct for a patient than by only visually study the images.In computed tomography (CT) whole-body images, several organs have been segmented using a multi-atlas approach. The fused labels are refined with a random forest classifier and a final graph cut segmentation. This method was evaluated in the VISCERAL Grand Anatomy Challenge and achieved the highest Dice score for 13 out of 20 organs. A development of this approach was done in order to achieve qualitatively better segmentations of the organs. Instead of fusing organ labels, a map of corresponding landmarks is obtained and the segmentation is given by the robust average of these with similar refinement steps as in the origin work. The segmentation results using this method is on par with or better than state-of-the-art. Segmentation of organs is important in e.g. radiotherapy planning.In another project with CT images, vertebrae have been detected and identified. This is useful in for instance surgical planning. The detection is done using convolutional neural networks. A shape model of the spine is fitted to the detections in order to correctly identify them. The task is difficult because, in general, only a limited part of the spine is visible. We are able to correctly identify 63 % of the vertebrae

Granskning av arbetsmiljö och olycksstatistik : NCC: s byggproduktion

Syftet med examensarbetet är att analysera och jämföra statistik över byggproduktionsrelaterade olyckor på NCC Construction Sverige AB och granska arbetsmiljöarbetet mellan regionerna Västerås och Uppsala för att se om det finns några skillnader. Arbetsmiljö är ett väldigt aktuellt ämne inom byggbranschen just nu. Informationen i examensarbetet grundas främst på information från arbetsmiljöverkets hemsida och böcker lånade på biblioteket och av examinatorn Gert Bard. Informationen om NCC: s arbetsmiljöarbete baseras på utförda intervjuer med platschefer och arbetsledare på tre byggarbetsplatser i Västerås och på tre byggarbetsplatser i Uppsala. Olycksstatistiken har erhållits från arbetsmiljöingenjören Rolf Tengnér på NCC Construction Sverige AB. Litteraturstudien omfattar en beskrivning av vad arbetsmiljö är och hur arbetsmiljöarbetet går till, vilka aktörer som medverkar och deras uppgifter. Litteraturstudien innehåller även en presentation av strategier som kan skapa bättre och friskare arbetsplatser. Resultatet innehåller en beskrivning av NCC: s arbetsmiljöarbete som består av arbetet med arbetsmiljöplan, arbetsmiljöpolicy, tillbudsrapportering, arbetsberedningar, skyddsronder och företagsvård. Resultatet omfattar även en beskrivning och en sammanställning av den olycksstatistik som erhållits från NCC Construction Sverige AB. Olyckstatistiken över Västerås och Uppsala sträcker sig från år 2005 t.o.m. år 2009. Statistiken har delats upp i olika kategorier för att det ska gå att jämföra de olika städerna och för att undersöka om det finns skillnader och likheter mellan olika kategorier. Svaren från enkätundersökningarna från de byggarbetsplatser som besöktes beskrivs och redovisas i tabell- och diagramform. Enkätundersökningen handlar om yrkesarbetarnas tillbudsrapportering, attityd, arbetsmiljö, ekonomi, stress, deras uppfattning om ledningens engagemang och faktorer som påverkar deras arbetsmiljö negativt.  Slutsatser och diskussion upplyser om de skillnader som upptäckts mellan regionerna Västerås och Uppsala. Olycksstatistiken visar bland annat att antalet olyckor är högre i Västerås än i Uppsala. Enkätundersökningarna visar exempelvis att yrkesarbetarna i Västerås känner sig mer stressade och är mindre engagerade i arbetsmiljöarbetet. Detta kan vara en av orsakerna till den högre olycksstatistiken i Västerås

A Measure of Septum Shape Using Shortest Path Segmentation in Echocardiographic Images of LVAD Patients

Patients waiting for heart transplantation due to a failing heart can get a left ventricular assist device (LVAD) implanted through open chest surgery. The device consists of a pump that pumps blood from the left ventricle into the aorta. To get the correct rotation speed of the pump, the physicians consider a number of measurements as well as a sequence of echocardiographic images. The important information obtained from the images is the shape of the inter-ventricular septum. For instance, if the septum bulges towards the left ventricle the speed is too high and it might harm the right ventricular function. To get a measure of the shape of the septum, which can be incorporated in a decision support system, we perform a segmentation of the septum using a shortest path method. To reduce user interaction, the user only needs to annotate two anchor points in the first frame. They mark the endpoints of the septum and they are tracked through the sequence with our tracking algorithm. After the segmentation the septum is divided into two regions, the one closest to the right ventricle and the one closest to the left ventricle, and the desired measure is the difference between the areas of these regions divided by the total septum area. The performance of the segmentation algorithm is acceptable and the obtained septum measure corresponds in most cases to the assessments from a physician

Segmentation of the Left Heart Ventricle in Ultrasound Images Using a Region Based Snake

Ultrasound imaging of the heart is a non-invasive method widely used for different applications. One of them is to measure the blood volume in the left ventricle at different stages of the heart cycle. This demands a proper segmentation of the left ventricle and a (semi-) automated method would decrease intra-variability as well as workload. This paper presents a semi-automated segmentation method that uses a region based snake. To avoid any unwanted concavities in the segmentations due to the cardiac valve we use two anchor points in the snake that are located to the left and to the right of the cardiac valve respectively. For the possibility of segmentations in different stages of the heart cycle these anchor points are tracked through the cycle. This tracking is based both on the resemblance of a region around the anchor points and a prior model of the movement in the y-direction of the anchor points. The region based snake functional is the sum of two terms, a regularizing term and a data term. It is our data term that is region based since it involves the integration of a two-dimensional subdomain of the image plane. A segmentation of the left ventricle is obtained by minimizing the functional which is done by continuously reshaping the contour until the optimal shape and size is obtained. The developed method shows promising results

Shape-aware multi-atlas segmentation

Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures

Shape-aware label fusion for multi-atlas frameworks

Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving topology and fine structures

Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies

Time-resolved medical data has important applications in a large variety of medical applications. In this paper we study automatic analysis of dynamical renal scintigraphies. The traditional analysis pipeline for dynamical renal scintigraphies is to use manual or semiautomatic methods for segmentation of pixels into physical compartments, extract their corresponding time-activity curves and then compute the parameters that are relevant for medical assessment. In this paper we present a fully automatic system that incorporates spatial smoothing constraints, compartment modelling and positivity constraints to produce an interpretation of the full time-resolved data. The method has been tested on renal dynamical scintigraphies with promising results. It is shown that the method indeed produces more compact representations, while keeping the residual of fit low. The parameters of the time activity curve, such as peak-time and time for half activity from peak, are compared between the previous semiautomatic method and the method presented in this paper. It is also shown how to obtain new and clinically relevant features using our novel system

An Automated System for the Detection and Diagnosis of Kidney Lesions in Children from Scintigraphy Images

Designing a system for computer aided diagnosis is a complex procedure requiring an understanding of the biology of the disease, insight into hospital workflow and awareness of available technical solutions. This paper aims to show that a valuable system can be designed for diagnosing kidney lesions in children and adolescents from 99m Tc-DMSA scintigraphy images. We present the chain of analysis and provide a discussion of its performance. On a per-lesion basis, the classification reached an ROC-curve area of 0.96 (sensitivity/specificity e.g. 97%/85%) measured using an independent test group consisting of 56 patients with 730 candidate lesions. We conclude that the presented system for diagnostic support has the potential of increasing the quality of care regarding this type of examination

Good Features for Reliable Registration in Multi-Atlas Segmentation

This work presents a method for multi-organ segmentation in whole-body CT images based on a multi-atlas approach. A robust and efficient feature-based registration technique is developed which uses sparse organ specific features that are learnt based on their ability to register different organ types accurately. The best fitted feature points are used in RANSAC to estimate an affine transformation, followed by a thin plate spline refinement. This yields an accurate and reliable nonrigid transformation for each organ, which is independent of initialization and hence does not suffer from the local minima problem. Further, this is accomplished at a fraction of the time required by intensity-based methods. The technique is embedded into a standard multi-atlas framework using label transfer and fusion, followed by a random forest classifier which produces the data term for the final graph cut segmentation. For a majority of the classes our approach outperforms the competitors at the VISCERAL Anatomy Grand Challenge on segmentation at ISBI 2015