Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study
Name
applsci-09-05296.pdf
Size
5.36 MB
Format
Adobe PDF
Checksum (MD5)
31b75f947d37499b084104011b9f271c
Author(s)
Vargas-Olivares, Arturo
Navarro-Hinojosa, Octavio
Pichardo, Samuel
Curiel, Laura
Alencastre Miranda, Moises
Chong-Quero, Jesús Enrique
Date Issued
December 2019
Journal
Applied Sciences
Publisher
MDPI AG
Citation
Vargas-Olivares, A.; Navarro-Hinojosa, O.; Pichardo, S.; Curiel, L.; Alencastre-Miranda, M.; Chong-Quero, J.E. Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study. Appl. Sci. 2019, 9, 5296. © 2019 The Author(s)
Version
Final published version
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. Keyword: F-measure; Ground truth; Image segmentation; MRgHIFU; Non-parametric statistics
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/app9245296
