Computerized cancer malignancy grading of fine needle aspirates

According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac

Registration and Deformable Model-Based Neck Muscles Segmentation and 3D Reconstruction

Whiplash is a very common ailment encountered in clinical practice that is usually a result of vehicle accidents but also domestic activities and sports injuries. It is normally caused when neck organs (specifically muscles) are impaired. Whiplash-associated disorders include acute headaches, neck pain, stiffness, arm dislocation, abnormal sensations, and auditory and optic problems, the persistence of which may be chronic or acute. Insurance companies compensate almost fifty percent of claims lodged due to whiplash injury through compulsory third party motor insurance. The morphological structures of neck muscles undergo hypertrophy or atrophy following damage caused to them by accidents. Before any medical treatment is applied , any such change needs to be known which requires 3D visualization of the neck muscles through a proper segmentation of them because the neck contains many other sensitive organs such as nerves, blood vessels, the spinal cord and trachea. The segmentation of neck muscles in medical images is a more challenging task than those of other muscles and organs due to their similar densities and compactness, low resolutions and contrast in medical images, anatomical variabilities among individuals, noise, inhomogeneity of medical images and false boundaries created by intra-muscular fat. Traditional segmentation algorithms, such as those used in thresholding and clustering-based methods, are not applicable in this project and also not suitable for medical images. Although there are some techniques available in clinical research for segmenting muscles such as thigh, tongue, leg, hip and pectoral ones, to the best of author's knowledge, there are no methods available for segmenting neck muscles due to the challenges described above. In the first part of this dissertation, an atlas-based method for segmenting MR images, which uses linear and non-linear registration frameworks, is proposed, with output from the registration process further refined by a novel parametric deformable model. The proposed method is tested on real clinical data of both healthy and non-healthy individuals. During the last few decades, registration- and deformable model-based segmentation methods have been very popular for medical image segmentation due to their incorporation of prior information. While registration-based segmentation techniques can preserve topologies of objects in an image, accuracy of atlas-based segmentation depends mainly on an effective registration process. In this study, the registration framework is designed in a novel way in which images are initially registered by a distinct 3D affine transformation and then aligned by a local elastic geometrical transformation based on discrete cosines and registered firstly slice-wise and then block-wise. The numbers of motion parameters are changed in three different steps per frame. This proposed registration framework can handle anatomical variabilities and pathologies by confining its parameters in local regions. Also, as warping of the framework relies on number of motion parameters, similarities between two images, gradients of floating image and coordinate mesh grid values, it can easily manage pathological and anatomical variabilities using a hierarchical parameter scheme. The labels transferred from atlas can be improved by deformable model-based segmentation. Although geometric deformable models have been widely used in many biomedical applications over recent years, they cannot work in the context of neck muscles segmentation due to noise, background clutter and similar objects touching each other. Another important drawback of geometric deformable models is that they are many times slower than parametric deformable ones. Therefore, the segmentation results produced by the registration process are ameliorated using a multiple-object parametric deformable model which is discussed in detail in the second part of this thesis. This algorithm uses a novel Gaussian potential energy distribution which can adapt to topological changes and does not require re-parameterization. Also, it incorporates a new overlap removal technique which ensures that there are no overlaps or gaps inside an object. Furthermore, stopping criteria of vertices are designed so that difference between boundaries of the deformable model and actual object is minimal. The multiple-object parametric deformable model is also applied in a template contours propagation-based segmentation technique, as discussed in the third part of this dissertation. This method is semi-automatic, whereby a manual delineation of middle image in a MRI data set is required. It can handle anatomical variabilities more easily than atlas-based segmentation because it can segment any individual's data irrespective of his/her age, weight and height with low computational complexity and it does not depend on other data as it operates semi-automatically. In it, initial model contour resides close to the object's boundary, with degree of closeness dependent on slice thicknesses and gaps between the slices

Rapid Segmentation Techniques for Cardiac and Neuroimage Analysis

Recent technological advances in medical imaging have allowed for the quick acquisition of highly resolved data to aid in diagnosis and characterization of diseases or to guide interventions. In order to to be integrated into a clinical work flow, accurate and robust methods of analysis must be developed which manage this increase in data. Recent improvements in in- expensive commercially available graphics hardware and General-Purpose Programming on Graphics Processing Units (GPGPU) have allowed for many large scale data analysis problems to be addressed in meaningful time and will continue to as parallel computing technology improves. In this thesis we propose methods to tackle two clinically relevant image segmentation problems: a user-guided segmentation of myocardial scar from Late-Enhancement Magnetic Resonance Images (LE-MRI) and a multi-atlas segmentation pipeline to automatically segment and partition brain tissue from multi-channel MRI. Both methods are based on recent advances in computer vision, in particular max-flow optimization that aims at solving the segmentation problem in continuous space. This allows for (approximately) globally optimal solvers to be employed in multi-region segmentation problems, without the particular drawbacks of their discrete counterparts, graph cuts, which typically present with metrication artefacts. Max-flow solvers are generally able to produce robust results, but are known for being computationally expensive, especially with large datasets, such as volume images. Additionally, we propose two new deformable registration methods based on Gauss-Newton optimization and smooth the resulting deformation fields via total-variation regularization to guarantee the problem is mathematically well-posed. We compare the performance of these two methods against four highly ranked and well-known deformable registration methods on four publicly available databases and are able to demonstrate a highly accurate performance with low run times. The best performing variant is subsequently used in a multi-atlas segmentation pipeline for the segmentation of brain tissue and facilitates fast run times for this computationally expensive approach. All proposed methods are implemented using GPGPU for a substantial increase in computational performance and so facilitate deployment into clinical work flows. We evaluate all proposed algorithms in terms of run times, accuracy, repeatability and errors arising from user interactions and we demonstrate that these methods are able to outperform established methods. The presented approaches demonstrate high performance in comparison with established methods in terms of accuracy and repeatability while largely reducing run times due to the employment of GPU hardware

Cluster analysis of the signal curves in perfusion DCE-MRI datasets

Pathological studies show that tumors consist of different sub-regions with more homogeneous vascular properties during their growth. In addition, destroying tumor's blood supply is the target of most cancer therapies. Finding the sub-regions in the tissue of interest with similar perfusion patterns provides us with valuable information about tissue structure and angiogenesis. This information on cancer therapy, for example, can be used in monitoring the response of the cancer treatment to the drug. Cluster analysis of perfusion curves assays to find sub-regions with a similar perfusion pattern. The present work focuses on the cluster analysis of perfusion curves, measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The study, besides searching for the proper clustering method, follows two other major topics, the choice of an appropriate similarity measure, and determining the number of clusters. These three subjects are connected to each other in such a way that success in one direction will help solving the other problems. This work introduces a new similarity measure, parallelism measure (PM), for comparing the parallelism in the washout phase of the signal curves. Most of the previous works used the Euclidean distance as the measure of dissimilarity. However, the Euclidean distance does not take the patterns of the signal curves into account and therefore for comparing the signal curves is not sufficient. To combine the advantages of both measures a two-steps clustering is developed. The two-steps clustering uses two different similarity measures, the introduced PM measure and Euclidean distance in two consecutive steps. The results of two-steps clustering are compared with the results of other clustering methods. The two-steps clustering besides good performance has some other advantages. The granularity and the number of clusters are controlled by thresholds defined by considering the noise in signal curves. The method is easy to implement and is robust against noise. The focus of the work is mainly the cluster analysis of breast tumors in DCE-MRI datasets. The possibility to adopt the method for liver datasets is studied as well

Proceedings of the International Workshop on Medical Ultrasound Tomography: 1.- 3. Nov. 2017, Speyer, Germany

Ultrasound Tomography is an emerging technology for medical imaging that is quickly approaching its clinical utility. Research groups around the globe are engaged in research spanning from theory to practical applications. The International Workshop on Medical Ultrasound Tomography (1.-3. November 2017, Speyer, Germany) brought together scientists to exchange their knowledge and discuss new ideas and results in order to boost the research in Ultrasound Tomography