In the present research work, image segmentation methods were studied to find internal parameters that provide an efficient identification of the regions of interest in Magnetic Resonance (MR) images used for the therapy planning of High-Intensity Focused Ultrasound (HIFU), a minimally invasive therapeutic method used for selective ablation of tissue. The involved image segmentation methods were threshold, level set and watershed segmentation algorithm with markers (WSAM), and they were applied to transverse and sagittal MR images obtained from an experimental setup of a murine experiment. A parametric study, involving segmentation tests with different values for the internal parameters, was carried out. The F-measure results from the parametric study were analyzed by region using Welch’s ANOVA followed by post hoc Games-Howell test to determine the most appropriate method for region identification. In transverse images, the threshold method had the best performance for the air region with a F-measure median of 0.9802 (0.9743–0.9847, interquartile range IQR 0.0104), the WSAM for the tissue, gel-pad, transducer and water region with a F-measure median of 0.9224 (0.8718–0.9468, IQR 0.075), 0.9553 (0.9496–0.9606, IQR 0.011), 0.9416 (0.9330–0.9540, IQR 0.021) and 0.9769 (0.9741–0.9803, IQR 0.0062), respectively. In sagittal images, threshold method had the best performance for the air region with a F-measure median of 0.9680 (0.9589–0.9735, IQR 0.0146), the WSAM for the tissue and gel-pad regions with a F-measure median of 0.9241 (0.8870–0.9426, IQR 0.0556) and 0.9553 (0.9472–0.9625, IQR 0.0153), respectively, and the Geodesic Active Contours (GAC) method for the transducer and water regions with a F-measure median of 0.9323 (0.9221–0.9402, IQR 0.0181) and 0.9681 (0.9627–0.9715, IQR 0.0088), respectively. The present research work integrates preliminary results to generate more efficient procedures of image segmentation for treatment planning of the MRgHIFU therapy. Future work will address the search of an automatic segmentation process, regardless of the experimental setup.
Modeling and simulation of the skeletal muscles are usually solved using the Finite Element method (FEM) which, although accurate, commonly needs a complex mesh and the solution is not processed in real-time. In this work, a meshfree model that simulates skeletal muscles considering their functioning and control based on electrical activity, their structure based on biological tissue, and that computes in real-time, is presented. Meshfree methods were used because they are able to surpass most of the limitations that are present in mesh-based methods. The muscular belly was modelled as a particle-based viscoelastic fluid, which is controlled using the monodomain model and shape matching. The smoothed particle hydrodynamics (SPH) method was used to solve both the fluid dynamics and the electrophysiological model. To analyze the accuracy of the method, a similar model was implemented with FEM. Both FEM and SPH methods provide similar solutions of the models in terms of pressure and displacement, with an error of around 0.09, with up to a 10% difference between them. Through the use of General-purpose computing on graphics processing units (GPGPU), real-time simulations that offer a viable alternative to mesh-based models for interactive biological tissue simulations was achieved.
Resumen. Con el auge de sensores de profundidad comerciales, como el Kinect, se crean nuevas oportunidades para desarrollar aplicaciones y sistemas interactivos que utilicen el cuerpo humano. Sin embargo, esos sensores generan una gran cantidad de datos en 3D (nubes de puntos) que tienen que ser procesados, buscando obtener obtener información relevante para aplicaciones específicas. Los algoritmos de agrupamiento, usualmente usados para minería de datos, sonútiles para descubrir esa información. Uno de los más conocidos es DBSCAN, que permite generar un número desconocido de grupos en un conjunto de datos, al mismo tiempo que filtra ruido. Sin embargo, también puede ser lento debido al tipo de dato que se use, así como a la búsqueda de datos con características similares. En este trabajo, se propone el uso de DBSCAN para procesar nubes de puntos. Adicionalmente, se propone una modificación del algoritmo, utilizando octrees para acelerar la búsqueda de vecinos que realiza, así como un esquema de particionamiento para que no se tengan que hacer búsquedas de vecinos de todos los puntos de una nube. Con el método propuesto, se logró acelerar considerablemente el procesamiento de nubes de puntos en comparación con el algoritmo original, logrando procesamiento en tiempo real, obteniendo los mismos resultados.Palabras clave: DBSCAN, particionamiento de datos, estructuras de datos espaciales, datos de profundidad, aplicaciones interactivas.Abstract. With the advent of commercial depth sensors, such as the Kinect, new opportunities to develop applications and interactive systems that use the human body are created. However, those sensors capture a large amount of 3D data, known as point clouds, that have to be processed, trying to obtain as much relevant information as possible. Clustering algorithms, normally used in data mining, are useful to
High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.
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