Abstract
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.
Highlights
Introduction1. Introduction Focused Ultrasound (HIFU) is a minimally invasive therapeutic method in which High-Intensity
The Geodesic Active Contours (GAC) method was the most suitable choice as it yielded a greater F-measure median value than the WSAM with a significant difference between the groups of results given by both methods
The present work was carried out to study the problem of segmentation in planning Magnetic Resonance (MR) images for the High-Intensity Focused Ultrasound (HIFU) therapy, using a murine model, to achieve an efficient identification of objects
Summary
1. Introduction Focused Ultrasound (HIFU) is a minimally invasive therapeutic method in which High-Intensity. Is a minimally invasive therapeutic method in which ultrasound beams are concentrated at a focal region, producing heating and selective ablation within ultrasound beams are concentrated at a focal region, producing heating and selective ablation within the focal volume without compromising surrounding tissues [1]. HIFU been proposed the safe ablation both without malignant and benignsurrounding tissues andtissues as an [1]. Resonance Imaging (MRI) has been proposed for guidance and monitoring of the HIFU therapy [2]. The. HIFU treatment is performed with an ultrasonic transducer, acoustically coupled to the target tissue. HIFU treatment is performed with an ultrasonic transducer, acoustically coupled to the target tissue usingusing water andand gel-pads, as as shown water gel-pads, shownininFigure Figure
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