ANALYSIS OF CIRCULATORY SYSTEM PATHOLOGIES IN HEAD CT DATA – HEMORRHAGE LOCALIZATION

Acute ischemic stroke and intracranial hemorrhages (ICH) represent critical situations for the patient. Rapid accurate diagnosis and therapy are required to prevent serious lifelong consequences or death. In the case of suspected head circulatory pathology, computed tomography (CT) is often the first choice among imaging techniques because of its availability, speed and reliability. In order to refine and speed up the diagnostic process, advanced algorithms implemented in computer aided diagnosis systems are currently being developed. This paper presents approaches to an automatic ICH localization as a part of a research project aimed at the development of machine learning methods for the analysis of circulatory disorders in head CT scans. Three designed deep learning-based algorithms are described and compared for prediction of the exact position of ICH within a 3D CT scan, and in two cases also for classification into the sub-types. An objective evaluation of the methods is presented along with a discussion of the results. Further possibilities for circulatory diseases analysis in head CT scans using modern algorithms are also discussed

Movement correction in thoracic dynamic contrast CT data

Tato práce se zabývá pružnou registrací obrazů pro korekci pohybů v 4D hrudních dynamických kontrastních CT datech. Disparitní analýzou založenou na přizpůsobené filtraci je inicializováno deformační pole určující lokální deformace, jehož hodnoty jsou poté zpřesněny optimalizačním algoritmem. Na základě charakteru pohybových zkreslení je aplikována FFD metoda s 3D geometrickou transformací B-splajn deformačním modelem. V první části je uvedena stručná teorie pro slícování obrazů. Ta je nezbytná pro porozumění dalším kapitolám této práce, které pojednávají o navržené metodě a její realizaci. Velká pozornost je věnována geometrické transformaci, která je velmi důležitou součástí procesu registrace. Popsán je zde i Nelder-Meadův algoritmus jako jedna z možných optimalizačních metod. Jsou zde uvedeny tři používané publikované algoritmy pro registraci 4D CT dat. V nadcházející kapitole jsou popsány jednotlivé dílčí části navržené pružné registrace, včetně možných problémů a jejich odstranění.This thesis deals with a nonrigid image registration for movement correction in thoracic dynamic contrast CT data. The deformation field is initialized by the analysis of disparities based on nonlinear matched filter, which defines local movement deformation. The values of control points are optimized by the Nelder-Mead method. The transformation model is based on a 4D (3D + time) free-form B-spline deformation for feature of movement distortion. The first part of the thesis briefly discusses the theory of image registration. Knowledge of this theory is necessary for understanding the remaining chapters, which describe the proposed method and its realization. The large part of this thesis is devoted to the geometrical image transformations, that is very important for the image registration. The thesis also describes a simplex method for function minimization. Three publicated methods of registration of medical 4D CT data are given. In the following chapter are individual parts of the purposed nonrigid registration including possible problems and their solution described.

Adaptive image sharpening

Tato bakalářská práce je rozborem problému a technickou zprávou k vytvořenému programu pro zvýraznění obrazů. Hlavním cílem celého procesu zpracování obrazu je adaptivní zostřování dosažené aplikací prostorově variantního lokálního konvolučního operátoru. Prostorová variantnost masky a tím stupeň ostření je určován místní směrodatnou odchylkou jasových hodnot v obraze. Rozhodnutí o ostření může být binární nebo spojité. V první části je uvedena stručná teorie k tématu. Ta je nezbytná pro další kapitoly této práce, které pojednávají o realizaci programu, popisují jednotlivé algoritmy, včetně blokových schémat a uživatelského prostředí a hodnotí dosažené výsledky včetně ukázek. V poslední kapitole se věnuji variantnosti obrázků a jejímu vlivu na kvalitu zostření a natavení parametrů procesu.This thesis is the analysis of the problem and technical report that supports a computer program for enhancement image. The main objective of picture processeng is adaptive sharpening, which is obtained by the application of a local convolutional operator. The decision on the degree of sharpening at individual pixels is based on the value of the local standard deviation of brightness. The degree of sharpening can take binary or continuous values. The first part of the report briefly discusses the theory of adaptive image sharpening. Knowledge of this theory is necessary for understanding the remaining chapters, which describe the individual algorithms including flows-diagrams, implementation of the program and graphical enviroment and also assess the achieved results, including demonstration on examples. The last section of the report deals with variability of images and it’s influence on settings parameters of sharpening.

Brain Vessels Enhancement in 3D CT Data Using Eigenvalues of Hessian Matrix

Cerebrovascular pathologies represent very serious life-threatening diseases and therefore great importance is attached to improving the diagnostic process of these diseases. In this work, an approach for brain vessels enhancement in 3D CT angiographic data has been proposed. A 3D binary mask of the brain was constructed and used for brain tissue extraction, in which the cerebral vessels were then enhanced using advanced filters based on Hessian matrix computation and analysis of Hessian eigenvalues. A dataset of 5 anonymized patient CT scans was used to design this approach

Methods of Segmentation and Identification of Deformed Vertebrae in 3D CT Data of Oncological Patients

In this doctoral thesis, the design of algorithms enabling the implementation of a fully automatic system for vertebrae segmentation in 3D computed tomography (CT) image data of possibly incomplete spines, in patients with bone metastases and vertebral compressions is presented. The proposed algorithm consists of several fundamental problems: spine detection and its axis determination, individual vertebra localization and identification (labeling), and finally, precise segmentation of vertebrae. The detection of the spine, specifically identifying its ends, and determining the course of the spinal canal, combines several advanced methods, including deep learning-based approaches. A novel growing circle method has been designed for tracing the spinal cord canal. Further, the innovative spatially variant filtering of brightness profiles along the spine axis leading to intervertebral disc localization has been proposed and implemented. The discs thus obtained are subsequently identified via comparing the tested vertebrae and model of vertebrae provided by a machine-learning process and optimized by dynamic programming. The final vertebrae segmentation is provided by the deformation of the complete-spine intensity model, utilizing a proposed multilevel registration technique. The complete proposed algorithm has been validated on testing databases, including also publicly available datasets. This way, it has been proven that the newly proposed algorithms provide results at least comparable to other author’s algorithms, and in some cases, even better. The main strengths of the algorithms lie in high reliability of the results and in the robustness to even strongly distorted vertebrae of oncological patients and to the occurrence of artifacts in data; moreover, they are capable of identifying the vertebra labels even in incomplete spinal CT scans. The strength is also in the complete automation of the processing and in its relatively low computational complexity enabling implementation on standard PC hardware. The system for fully automatic localization and labeling of distorted vertebrae in possibly incomplete spinal CT data is presented in this doctoral thesis. The design of algorithms enabling the implementation utilizes several novel approaches, which were presented at international conferences and published in the journal Jakubicek et al. (2020). Based on the results of the experimental validation, the proposed algorithms seem to be routinely usable and capable of providing fully acceptable input data (identified and precisely segmented vertebrae) as needed in the subsequent automatic spine bone lesion analysis