Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study
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 ther...
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MDPI AG
2020
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| Online Access: | https://hdl.handle.net/1721.1/123876 |
| _version_ | 1811095073184546816 |
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| author | Vargas-Olivares, Arturo Navarro-Hinojosa, Octavio Pichardo, Samuel Curiel, Laura Alencastre Miranda, Moises Chong-Quero, Jesús Enrique |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Vargas-Olivares, Arturo Navarro-Hinojosa, Octavio Pichardo, Samuel Curiel, Laura Alencastre Miranda, Moises Chong-Quero, Jesús Enrique |
| author_sort | Vargas-Olivares, Arturo |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T16:10:12Z |
| format | Article |
| id | mit-1721.1/123876 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:10:12Z |
| publishDate | 2020 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1238762022-10-02T06:50:35Z Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study Vargas-Olivares, Arturo Navarro-Hinojosa, Octavio Pichardo, Samuel Curiel, Laura Alencastre Miranda, Moises Chong-Quero, Jesús Enrique Massachusetts Institute of Technology. Department of Mechanical Engineering 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 Consejo Nacional de Ciencia y Tecnología (Mexico) (Award 419184) 2020-02-27T21:17:10Z 2020-02-27T21:17:10Z 2019-12 2019-11 Article http://purl.org/eprint/type/JournalArticle 2076-3417 https://hdl.handle.net/1721.1/123876 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) http://dx.doi.org/10.3390/app9245296 Applied Sciences Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG MDPI |
| spellingShingle | Vargas-Olivares, Arturo Navarro-Hinojosa, Octavio Pichardo, Samuel Curiel, Laura Alencastre Miranda, Moises Chong-Quero, Jesús Enrique Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study |
| title | Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study |
| title_full | Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study |
| title_fullStr | Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study |
| title_full_unstemmed | Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study |
| title_short | Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study |
| title_sort | image segmentation for the treatment planning of magnetic resonance guided high intensity focused ultrasound mrghifu therapy a parametric study |
| url | https://hdl.handle.net/1721.1/123876 |
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