Browsing by Author "Oralkan, Ömer"

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    Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging
    (MDPI, 2023-09-08) Wu, Xun; Sanders, Jean L.; Dundar, M. Murat; Oralkan, Ömer; Computer and Information Science, School of Science
    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.
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    Photons Plus Ultrasound: Imaging and Sensing 2017, International Society for Optics and Photonics
    (SPIE, 2017-03) Wu, Xun; Sanders, Jean; Dundar, Murat; Oralkan, Ömer; Computer and Information Science, School of Science
    Photoacoustic imaging (PAI) can be used to monitor lesion formation during high-intensity focused ultrasound (HIFU) therapy because HIFU changes the optical absorption spectrum (OAS) of the tissue. However, in traditional PAI, the change could be too subtle to be observed either because the OAS does not change very significantly at the imaging wavelength or due to low signal-to-noise ratio in general. We propose a machine-learning-based method for lesion monitoring with multi-wavelength PAI (MWPAI), where PAI is repeated at a sequence of wavelengths and a stack of multi-wavelength photoacoustic (MWPA) images is acquired. Each pixel is represented by a vector and each element in the vector reflects the optical absorption at the corresponding wavelength. Based on the MWPA images, a classifier is trained to classify pixels into two categories: ablated and non-ablated. In our experiment, we create a lesion on a block of bovine tissue with a HIFU transducer, followed by MWPAI in the 690 nm to 950 nm wavelength range, with a step size of 5 nm. In the MWPA images, some of the ablated and non-ablated pixels are cropped and fed to a neural network (NN) as training examples. The NN is then applied to several groups of MWPA images and the results show that the lesions can be identified clearly. To apply MWPAI in/near real-time, sequential feature selection is performed and the number of wavelengths is decreased from 53 to 5 while retaining adequate performance. With a fast-switching tunable laser, the method can be implemented in/near real-time.