Pilvo aortos vietos nustatymas krūtinės ląstos tomografinėje nuotraukoje

Kompiuterinė tomografija, kuri naudojama pilvo aortos aneurizmos diagnostikai ir stebėjimui, leidžia vartotojams stebėti aneurizmos būseną paciento kūno skerspjūvio nuotraukų sekoje. Dažnai kompiuterine tomografija grįstiems diagnostikos įrankiams aortos vieta turi būti nurodoma vartotojo. Darbe aprašomas pilvo aortos vidinių taškų identifikavimo būdas be pradinės aortos vietos išankstinio žymėjimo. Darbe sprendžiami uždaviniai: medicinos vaizdų analizės metodų apžvalga bei parinkimas, pirminis vaizdų apdorojimas pašalinant triukšmą bei išskiriant vaizde esančių objektų kontūrus, aortos aptikimo metodo pasiūlymas. Taip pat pasiūlomas aortos trombo aptikimo metodas, kuriam vartotojas turi pateikti sekos vaizdą, kuriame trombas yra matomas, bet jam nereikia nurodyti tikslios ar apytikslės trombo vietos

A survey on brain tumor diagnosis and edema detection based on machine learning

Early brain tumor diagnosis has a significant role in reducing the risk of disease, as well as led to get better treatment results. Usually, magnetic resonance imaging (MRI) images are evaluated manually through visual inspection, which is difficult, time-consuming and often erroneous;this process is performed by radiologists or clinical experts, and its accuracy depends on their experience. Recently, computer-aided diagnosis (CAD) becomes very essential to overcome these limitations. This paper provides a comprehensive assessment of the existing techniques and methodologies for automated detection of brain tumor coupled with oedema detection methods utilisation, with an emphasis on machine learning models. Moreover, this paper provides an analysis of the integrated procedure that pertains to the retrieval of brain pictures by identifying particular data sets in the procedure to recognise the stipulated attributes

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue

2D and 3D segmentation of medical images.

"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.

Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou

Prenatal ultrasound induces apoptotic neurons and Glial Cells (AC) in rabbit fetal brain: a biochemical analysis

Ultrasound wave propagates through tissues are absorbed and converted to heat. Findings in numerous studies utilizing the ultrasound exposure have also convinced the existence of ultrasound-induced apoptosis in the exposed cells. Hence, this current study was aimed to detect the apoptotic neurons and glial cells (AC) in the rabbit fetal brain resulting from the prenatal ultrasound exposure. The terminal dUTP nick end-labeling (TUNEL) assay staining by ApopTag® Plus Peroxidase In-Situ Apoptosis Detection Kit (S7101) from Millipore, USA was used. The effects of prenatal ultrasound to neurons and glial cells were analyzed by comparing the AC counts in the rabbit fetal cerebral cross section between the expose and control groups. At least 3 TUNEL stained slides were randomly examined from each subject making a total of N=102 (30 minutes exposure, n= 9; 60 minutes exposure, n= 9; 90 minutes exposure, n= 9; control, n= 7). The temperature increment was measured during the prenatal ultrasound exposure, which the maximum was 1.0, 1.8 and 3.3°C for 30, 60 and 90 minutes of exposure, respectively. Data was analyzed using SPSS version 21.0. The p-values were significant at all stages of gestation with all the p-values were less than 0.001 (p<0.001). The results suggested that there were significant differences in AC counts in all stages of gestation between groups of different exposure duration. The detection of the DNA fragmentation in TUNEL positive cell could serve as an evidence in suggesting the apoptosis was induced by the ultrasound exposure

Upper airways segmentation using principal curvatures

Esta tesis propone una nueva técnica para segmentar las vías aéreas superiores. Esta propuesta permite la extracción de estructuras curvilíneas usando curvaturas principales. La propuesta permite la extracción de éstas estructuras en imágenes 2D y 3D. Entre las principales novedades se encuentra la propuesta de un nuevo criterio de parada en la propagación del algoritmo de realce de contraste (operador multi-escala de tipo sombrero alto). De la misma forma, el criterio de parada propuesto es usado para detener los algoritmos de difusión anisotrópica. Además, un nuevo criterio es propuesto para seleccionar las curvaturas principales que conforman las estructuras curvilíneas, que se basa en los criterios propuestos por Steger, Deng et. al. y Armande et. al. Además, se propone un nuevo algoritmo para realizar la supresión de nomáximos que permite reducir la presencia de discontinuidades en el borde de las estructuras curvilíneas. Para extraer los bordes de las estructuras curvilíneas, se utiliza un algoritmo de enlace que incluye un nuevo criterio de distancia para reducir la aparición de agujeros en la estructura final. Finalmente, con base en los resultados obtenidos, se utiliza un algoritmo morfológico para cerrar los agujeros y se aplica un algoritmo de crecimiento de regiones para obtener la segmentación final de las vías respiratorias superiores.This dissertation proposes a new approach to segment the upper airways. This proposal allows the extraction of curvilinear structures based on the principal curvatures. The proposal allows extracting these structures from 2D and 3D images. Among the main novelties is the proposal of a new stopping criterion to stop the propagation of the contrast enhancement algorithm (multiscale top-hat morphological operator). In the same way, the proposed stopping criterion is used to stop the anisotropic diffusion algorithms. In addition, a new criterion is proposed to select the principal curvatures that make up the curvilinear structures, which is based on the criteria proposed by Steger, Deng et. al. and Armande et. al. Furthermore, a new algorithm to perform the non-maximum suppression that allows reducing the presence of discontinuities in the border of curvilinear structures is proposed. To extract the edges of the curvilinear structures, a linking algorithm is used that includes a new distance criterion to reduce the appearance of gaps in the final structure. Finally, based on the obtained results, a morphological algorithm is used to close the gaps and a region growing algorithm to obtain the final upper airways segmentation is applied.Doctor en IngenieríaDoctorad

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

Investigation of conserved Flagellum proteins in Trypanosoma brucei

The single celled protozoan parasite Trypanosoma brucei is an excellent model organism to study eukaryotic cilia and flagella as it has a single flagellum that remains assembled throughout the cell cycle. The new flagellum assembles in a known position relative to the old flagellum, therefore creating a model system of identifiable organelle generations. In additional to a sequenced genome, there are many reverse genetics tools developed for T. brucei which makes the functional analysis of proteins possible. More than 300 proteins have been identified as components of the T. brucei flagellum but functional analysis of the majority of these proteins has not been carried out to date. This project used a bioinformatics approach to identify potential flagellum proteins in T. brucei that were also conserved in Homo sapiens, thereby identifying potential ciliopathy candidates. Candidate proteins were confirmed as flagellum components through endogenous localisation techniques and co-localisation studies. Functional analysis was performed using inducible RNAi cell lines. Light and electron microscopy techniques were used for phenotypic analysis. Through bioinformatics analysis a novel family of coiled-coil TPH domain-containing proteins were identified that are highly conserved in flagellated eukaryotes. There are three TPH domain-containing proteins conserved in T. brucei that all have a role in flagellum length control and cell morphogenesis. In all three cases protein ablation has a detrimental effect on cellular motility. This work provides further understanding into the complexities of flagellum biogenesis in T. brucei and the downstream effects on cell motility and morphogenesis