Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging
Photoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoustic (MWPA) imaging makes this change easier to dete...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/9/1060 |
| _version_ | 1797581233003167744 |
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| author | Xun Wu Jean L. Sanders M. Murat Dundar Ömer Oralkan |
| author_facet | Xun Wu Jean L. Sanders M. Murat Dundar Ömer Oralkan |
| author_sort | Xun Wu |
| collection | DOAJ |
| description | Photoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoustic (MWPA) imaging makes this change easier to detect by repeating PA imaging at multiple optical wavelengths and sampling the optical absorption spectrum more thoroughly. Real-time pixel-wise classification in MWPA imaging can assist clinicians in monitoring HIFU lesion formation and will be a crucial milestone towards full HIFU therapy automation based on artificial intelligence. In this paper, we present a deep-learning-based approach to segment HIFU lesions in MWPA images. Ex vivo bovine tissue is ablated with HIFU and imaged via MWPA imaging. The acquired MWPA images are then used to train and test a convolutional neural network (CNN) for lesion segmentation. Traditional machine learning algorithms are also trained and tested to compare with the CNN, and the results show that the performance of the CNN significantly exceeds traditional machine learning algorithms. Feature selection is conducted to reduce the number of wavelengths to facilitate real-time implementation while retaining good segmentation performance. This study demonstrates the feasibility and high performance of the deep-learning-based lesion segmentation method in MWPA imaging to monitor HIFU lesion formation and the potential to implement this method in real time. |
| first_indexed | 2024-03-10T23:01:23Z |
| format | Article |
| id | doaj.art-635293255ef349ab88588ebf03eecab6 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T23:01:23Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-635293255ef349ab88588ebf03eecab62023-11-19T09:37:08ZengMDPI AGBioengineering2306-53542023-09-01109106010.3390/bioengineering10091060Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic ImagingXun Wu0Jean L. Sanders1M. Murat Dundar2Ömer Oralkan3Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USADepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USAComputer and Information Science Department, Indiana University—Purdue University, Indianapolis, IN 46202, USADepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USAPhotoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoustic (MWPA) imaging makes this change easier to detect by repeating PA imaging at multiple optical wavelengths and sampling the optical absorption spectrum more thoroughly. Real-time pixel-wise classification in MWPA imaging can assist clinicians in monitoring HIFU lesion formation and will be a crucial milestone towards full HIFU therapy automation based on artificial intelligence. In this paper, we present a deep-learning-based approach to segment HIFU lesions in MWPA images. Ex vivo bovine tissue is ablated with HIFU and imaged via MWPA imaging. The acquired MWPA images are then used to train and test a convolutional neural network (CNN) for lesion segmentation. Traditional machine learning algorithms are also trained and tested to compare with the CNN, and the results show that the performance of the CNN significantly exceeds traditional machine learning algorithms. Feature selection is conducted to reduce the number of wavelengths to facilitate real-time implementation while retaining good segmentation performance. This study demonstrates the feasibility and high performance of the deep-learning-based lesion segmentation method in MWPA imaging to monitor HIFU lesion formation and the potential to implement this method in real time.https://www.mdpi.com/2306-5354/10/9/1060multi-wavelength photoacoustic imaginghigh-intensity focused ultrasound therapylesion segmentationdeep learningmachine learningconvolutional neural network |
| spellingShingle | Xun Wu Jean L. Sanders M. Murat Dundar Ömer Oralkan Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging Bioengineering multi-wavelength photoacoustic imaging high-intensity focused ultrasound therapy lesion segmentation deep learning machine learning convolutional neural network |
| title | Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging |
| title_full | Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging |
| title_fullStr | Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging |
| title_full_unstemmed | Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging |
| title_short | Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging |
| title_sort | deep learning based high intensity focused ultrasound lesion segmentation in multi wavelength photoacoustic imaging |
| topic | multi-wavelength photoacoustic imaging high-intensity focused ultrasound therapy lesion segmentation deep learning machine learning convolutional neural network |
| url | https://www.mdpi.com/2306-5354/10/9/1060 |
| work_keys_str_mv | AT xunwu deeplearningbasedhighintensityfocusedultrasoundlesionsegmentationinmultiwavelengthphotoacousticimaging AT jeanlsanders deeplearningbasedhighintensityfocusedultrasoundlesionsegmentationinmultiwavelengthphotoacousticimaging AT mmuratdundar deeplearningbasedhighintensityfocusedultrasoundlesionsegmentationinmultiwavelengthphotoacousticimaging AT omeroralkan deeplearningbasedhighintensityfocusedultrasoundlesionsegmentationinmultiwavelengthphotoacousticimaging |