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Identification of vertebrae type in CT data by machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Identification of vertebrae type by machine learning is an important task to facilitate the work of medical doctors. This task is embarrassed by many factors. First, a spinal CT imagining is usually performed on patiens with pathologies such as lesions, tumors, kyphosis, lordosis, scoliosis or patients with various implants that cause artifacts in the images. Furthermore, the neighboring vertebraes are very similar which also complicates this task. This paper deals with already segmented vertebrae classification into cervical, thoracic and lumbar groups. Support vector machines (SVM) and convolutional neural networks (CNN) AlexNet and VGG16 are used for classification. The results are compared in the conclusion.
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Methods of Detection, Segmentation and Classification of Difficult to Define Bone Tumor Lesions in 3D CT Data
Chmelík, Jiří ; Flusser,, Jan (referee) ; Kozubek, Michal (referee) ; Jan, Jiří (advisor)
The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.
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Segmentation of cortical parts of vertebrae
Janštová, Michaela ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with a segmentation of cortical parts of vertebrae from CT image datas in programming software called MATALB. Issues about segmentation techniques are described, especially „level-set” method and its modification DRLSE. This method was chosen because of informations from articles published in spcialized publications and also thanks to its plentiful usage and satisfactory results. In the end of this paper is designed method tested on real CT datas.
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Automatic segmentation of regions of interest in a human vertebra
Novosadová, Michaela ; Jan, Jiří (referee) ; Peter, Roman (advisor)
This bachelor´s thesis describes anatomy of the spine and the most frequent pathologies of the spine with focus on those tumour diseases, that affect more and more people today. The other part of the work describes theory of image registration. The aim of this thesis is to create an algorithm able to do automatic segmentation of regions of interest in human vertebra (body and posterior elements). This segmentation can simplify the classification of tumour diseases of the spine in the future. A solution was designed on the base of theoretical knowledge. This solution is based on registration of segmented models on original vertebrae. The thesis also describes the process of the solution. For easier understanding, the process of solution and the evaluation of results are added with number of graphs, images and tables.
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Detection of pathological vertebrae in spinal CTs utilised by machine learning methods
Tyshchenko, Bohdan ; Ronzhina, Marina (referee) ; Chmelík, Jiří (advisor)
This master's thesis focuses on detection of pathological vertebrae in spinal CT utilized by machine learning. Theoretical part describes anatomy of the spine and occurrence of pathologies in CT image data, contains an overview of existing methods intended for automated detection of pathological vertebrae. Practical part devotes to design a computer aided detection systems to identify pathological vertebrae and to classify a type of pathology. Designed classification system is based on using neural network, which performs classification step and on principal component analysis (PCA), which is used to reducing the original number of observation features. For completing this task were used real data. Conclusion contains evaluation of obtained results.
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Vertebra detection and identification in CT oncological data
Věžníková, Romana ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
Automated spine or vertebra detection and segmentation from CT images is a difficult task for several reasons. One of the reasons is unclear vertebra boundaries and indistinct boundaries between vertebra. Next reason is artifacts in images and high degree of anatomical complexity. This paper describes the design and implementation of vertebra detection and classification in CT images of cancer patients, which adds to the complexity because some of vertebrae are deformed. For the vertebra segmentation, the Otsu’s method is used. Vertebra detection is based on search of borders between individual vertebra in sagittal planes. Decision trees or the generalized Hough transform is applied for the identification whereas the vertebra searching is based on similarity between each vertebra model shape and planes of CT scans.
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Effect of spine on stresses in abdominal aortic aneurysm
Lisický, Ondřej ; Horný,, Lukáš (referee) ; Polzer, Stanislav (advisor)
This thesis deals with stress strain analysis of an aortic abdominal aneurysm (AAA) and the influence of its contact with the spine on the extreme wall stress. The influence was tested on the idealized geometry, as well as on ten patient specific geometries obtained from computer tomography (CT-A) scans. Hyperelastic constitutive models were used for the AAA wall and intraluminal thrombus (ILT) tissue description. The prestress algorithm was used for reconstruction of the unloaded geometry to get more trustworthy results against the geometry from CT which was obtained under the blood pressure. Statistical analysis was used for the results evaluation. The maximal increase of peak wall stress was as high as 81 %.
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Implementation of a deep learning model for vertebral segmentation in CT data
Blažková, Lenka ; Chmelík, Jiří (referee) ; Nohel, Michal (advisor)
This bachelor’s thesis deals with the problem of vertebrae segmentation in CT data with the use of deep learning. Firstly, there is a theoretical review focused on the anatomy and the pathologies of the spine and the vertebrae, the CT systems, and the deep learning models for vertebrae segmentation in 3D data. The following section contains a more detailed description of the chosen model. The fifth section describes the implementation of the chosen model and the proposed modification, together with the results on the relevant database. In the end, the model with the modification is used on the clinical data provided by the supervisor and its evaluation is described.
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Feasibility of the lumbar area disc hernition conservative therapy in the view of a physiotherapist
Reichová, Eva ; Horká, Bohumila (advisor) ; Němečková, Michaela (referee)
Author: Eva Reichová Institution: Department of Rehabilitation Medicine, Faculty of Medicine in Hradec Králové, Charles University in Prague, Czech Republic Title: Feasibility of Lumbar Area Disc Herniation Conservative Therapy in the View of Physiotherapist Supervisor: Mgr. Bohumila Horká Number of pages: 94 Year of defence: 2012 Keywords: Hernia, intervertebral disc, lumbr spine, vertebra, radicular syndrome The bachelor's thesis deals with possibilities of the kinesiotherapy in the lumbar disk hernia, which is one of the most frequent degenerative diseases of the spine. Theoretical part deals with (1) the spine anatomy, kinesiology, and biomechanics, (2) ethiology and diagnostics of the disc hernia, and (3) feasibility of its conservative therapy using McKenzie's method, activation of the deep stabilizing system of the spine, and reflex locomotion method according Vojta. I the practical part, two case reports of the lumbar disk hernia are discussed with emphasis on the entrance medical and physiotherapeutic checkup, short-term therapeutic plan, therapy, exit checkup, and long-term therapeutic plan.
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Methods of Detection, Segmentation and Classification of Difficult to Define Bone Tumor Lesions in 3D CT Data
Chmelík, Jiří ; Flusser,, Jan (referee) ; Kozubek, Michal (referee) ; Jan, Jiří (advisor)
The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.
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