Patient’s respiratory curve synchronization by visual feedback application

In radiation therapy, irradiation systems are focused to target using a CT scan. In the case of target which is moving by breathing, the CT scan is performed in the patient's inhale or exhale. However, ensuring that the patient is breathing same way as during the CT scan is really problematic. This project deals with the issue by using special respiratory sensor and software application, which presents respiratory curve to patient via video goggles. During the planning CT scan, the patient's breathing curve is captured by a special sensor and is displayed using a software application that allows insertion of the limit markers, which are represented by stripes, which indicate how much the patient inhaled. The application has possibilities to store the location of stripes during CT scan and retrieve it again during a scheduled radiotherapy, when patient can surely breathe the same way as during a planning CT scan. The application was developed, implemented and successfully tested at the Oncology Clinic of the Faculty Hospital Ostrava

Design and Implementation of an Algorithm for the System Limit Radiation

Import 22/07/2015Práce popisuje realizaci aplikace pro efektivní plánování procesu radioterapie u systému CyberKnife pomocí zpracování CT snímků. Systém CyberKnife, který zaměřuje terapeutický cíl pomocí dvou rentgenek, jež snímkují pacienta pod úhlem 45 stupňů, umožňuje v oblasti plic lokalizaci ozařovaného nádorového ložiska na základě rozdílu denzit (míry absorpce záření) mezi ložiskem a okolní tkání. Některá ložiska je obtížné lokalizovat vzhledem k sumaci a tedy překryvu sledované oblasti s jinými vysoce denzitními strukturami (např. páteř, paže, žebra). Úkolem práce bylo vytvoření snímků z úhlu 45 stupňů, kde je následně v aplikaci simulována rotace pacienta na CT tak, aby byl překryv nežádoucích tkáňových struktur eliminován. Aplikace je řešena jako klient vytvořený v jazyce C#, připojený na COM server výpočetního systému MATLAB, který zajišťuje většinu výpočtů. Doplňující zpracování obrazu je zajišťováno přímo v klientu, implementováno v jazyce C#. Systém byl testován na jedno jádrovém systému čipů a rychlost načtení a zpracování se pohybuje průměrně kolem 1 s. Systém byl vyvíjen, realizován a nyní úspěšně testován na Onkologické klinice FN Ostrava.This thesis describes the implementation of application for effective planning process of CyberKnife system by processing CT images. CyberKnife system, which focuses therapeutic target using two X-ray tubes and takes images of the patient at an angle of 45 degrees, allows localization of lung irradiated tumor bearing based on the difference in density (absorption rate of radiation) between the bearing and the surrounding tissue. Some tumor bearings are difficult to localize due to summation and overlay of monitoring area with other high densities structures (e.g. the spine, arms and ribs). The task of the work was to create images from an angle of 45 degrees with simulated rotation of patient during CT until the overlay is eliminated. The application is designed as a client created in C#, connected to the COM server of computing system MATLAB, which provides most of the calculations. Additional image processing is provided directly in the client, implemented in C#. The application was tested on a single core chip system and speed of acquisition and processing is in average around 1 s. The application was developed, implemented and now successfully tested on the Oncology Clinic at FN Ostrava.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Batch Processing of Biomedical Calculations on a Rivyera Supercomputer

Práce pojednává o problematice dávkového zpracování dat na hardwarové architektuře hradlových polí zastoupené superpočítačem Rivyera, jehož výpočetní výkon je zprostředkován několika desítkami programovatelných hradlových polí. Cílem práce je navrhnout a realizovat řešení vzdálené obsluhy a dávkové zpracování dat na superpočítači Rivyera. A dále také poskytnout postup pro vývoj a řešení výpočtů s použitím paralelního přístupu na superpočítači Rivyera. Výpočetní postupy jsou řešeny kombinací výpočetního jádra na programovatelných hradlových polích, popsaných v jazyce VHDL, a obslužné hostitelské aplikace v programovacím jazyce Java. Ukázkou výpočetních postupů s použitím superpočítače Rivyera je paralelní zpracování CT obrazů do podoby digitálně rekonstruovaného rentgenového snímku z určité směrové projekce.This thesis describes batch processing of data at hardware architecture of gate arrays represented by Rivyera supercomputer, its computational power is mediated by several dozens of field programmable gate arrays. The aim of thesis is to design and implement solution of remote control and batch processing of data at Rivyera supercomputer and also to provide a method to development and resolution of calculation by using parallel computing of Rivyera supercomputer. Computational methods are solved by combining of computational core of field programmable gate arrays, described using VHDL language, and handler host application, written in Java programming language. An example of computational methods using Rivyera supercomputer is parallel processing of CT images into digitally reconstructed radiographs from a certain directional projection.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Evaluation of an electro-pneumatic device for artificial capillary pulse generation used in a prospective study in animals for surgical neck wound healing

The paper examines the development and testing of an electro-pneumatic device for wound healing therapy after surgery in the neck area. The device generates air pressure values in a miniaturized cuff using electronic circuitry to drive an electro-valve and air compressor. The device works in two distinct modes: continuous pressure mode and pulsating pressure mode. The pressure value setting can vary from 3 to 11 mmHg, and the pulsating pressure mode's operating frequency range is approximately 0.1 to 0.3 Hz. Laboratory measurements were conducted to evaluate the device's correct functioning in both continuous and pulsating pressure modes. A four-day prospective study with animals (n = 10) was also conducted to evaluate neck wound healing therapy using the electro-pneumatic device. Out of the twelve histological parameters analysed to reveal the differences between the experimental and control wounds, only one demonstrated a significant difference. Out of the ten animals treated with the device, three showed a significant difference in terms of benefit after therapy. We can therefore conclude that the device potentially improves the wound healing process in the neck area if the pre-set air pressure value does not exceed 8 mmHg.Web of Science9art. no. 983

Detection and Segmentation of Retinal Lesions in Retcam 3 Images Based on Active Contours Driven by Statistical Local Features

Clinical retinal image analysis is an import aspect of clinical diagnosis in ophthalmology. Retinopathy of Prematurity (ROP) represents one of the most severe retinal disorders in prematurely born infants. One of the ROP clinical signs is the presence of retinal lesions endangering the vision system. Unfortunately, the stage and progress of these findings is often only subjectively estimated. A procedure such as this is undoubtedly linked to subjective inaccuracies depending on the experience of the ophthalmologist. In our study, a fully autonomous segmentation algorithm to model retinal lesions found using RetCam 3 is proposed. The proposed method used a combination of retinal image preprocessing and active contours for retinal lesion segmentation. Based on this procedure, a binary model of retinal lesions that allowed retinal lesions to be classified from a retinal image background was obtained. Another important aspect of the model was feature extraction. These features reliably and automatically described the development stage of an individual lesion. A complex procedure such as this has significant implications for ophthalmic clinical practice in substituting manual clinical procedures and improving the accuracy of routine clinical decisions

Performance and robustness of regional image segmentation driven by selected evolutionary and genetic algorithms: Study on MR articular cartilage images

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.Web of Science2217art. no. 633

Development of methodology for measurement and evaluation of stomach electrical activity

Tato práce se zabývá návrhem metodiky měření a vyhodnocení elektrické aktivity žaludku s cílem poskytnout podporu diagnostiky fyziologického stavu žaludku. Výstupy práce jsou metodika měření, skládající se z definovaného minimalizovaného rozložení elektrod, pravidel snímání a metod předzpracování, a metodika vyhodnocení naměřených dat, která popisuje postupy pro extrakci klasifikátorů a realizovaný fuzzy expertní systém navržený pro jejich vyhodnocení. Za účelem zajištění testovacích dat pro analýzu, návrh a validaci principů a postupů při návrhu obou metodik bylo provedeno několik experimentálních měření elektrické aktivity žaludku, která byla realizována na dobrovolnících. Navržený fuzzy expertní systém byl vytvořen na základě statistického vyhodnocení naměřených dat a byl konzultován s odborníky z klinické praxe. Spolehlivost systému byla ověřena simulacemi s umělými daty i vyhodnocením reálných signálů, přičemž systém v obou případech vyhověl a může být použit pro další ověření v klinické praxi.This work deals with proposal of methodology of measurement and evaluation of electrical activity of stomach to offer support of diagnosis of physiological state of stomach. Outcomes of this work are methodology of measurement consisting of defined minimized electrodes layout, measurement rules and pre-processing methods, and methodology of evaluation of measured signals describing classifiers extraction procedures and their analysis using proposed fuzzy inference system. Several experimental measurements were conducted on volunteers in order to collect testing data for analysis, design and validation of principals and procedures of both methodologies. Proposed fuzzy inference system was designed based on statistical evaluation of measured data and discussion with experts from clinical environment. Reliability of the system was validated by simulations using artificial data and by evaluation of real data while in both cases the system complied, therefore it can be used for another validation in clinical practice.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově

Novel Hybrid Optimized Clustering Schemes with Genetic Algorithm and PSO for Segmentation and Classification of Articular Cartilage Loss from MR Images

Medical image segmentation plays an indispensable role in the identification of articular cartilage, tibial and femoral bones from magnetic resonance imaging (MRI). There are various image segmentation strategies that can be used to identify the knee structures of interest. Among the most popular are the methods based on non-hierarchical clustering, including the algorithms K-means and fuzzy C-means (FCM). Although these algorithms have been used in many studies for regional image segmentation, they have two essential drawbacks that limit their performance and accuracy of segmentation. Firstly, they rely on a precise selection of initial centroids, which is usually conducted randomly, and secondly, these algorithms are sensitive enough to image noise and artifacts, which may deteriorate the segmentation performance. Based on such limitations, we propose, in this study, two novel alternative metaheuristic hybrid schemes: non-hierarchical clustering, driven by a genetic algorithm, and Particle Swarm Optimization (PSO) with fitness function, which utilizes Kapur’s entropy and statistical variance. The goal of these optimization elements is to find the optimal distribution of centroids for the knee MR image segmentation model. As a part of this study, we provide comprehensive testing of the robustness of these novel segmentation algorithms upon the image noise generators. This includes Gaussian, Speckle, and impulsive Salt and Pepper noise with dynamic noise to objectively report the robustness of the proposed segmentation strategies in contrast with conventional K-means and FCM. This study reveals practical applications of the proposed algorithms for articular cartilage extraction and the consequent classification performance of early osteoarthritis based on segmentation models and convolutional neural networks (CNN). Here, we provide a comparative analysis of GoogLeNet and ResNet 18 with various hyperparameter settings, where we achieved 99.92% accuracy for the best classification configuration for early cartilage loss recognition