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Wang Y, Chen Y, Zhao Y, Liu S. Compressed Sensing for Biomedical Photoacoustic Imaging: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2670. [PMID: 38732775 PMCID: PMC11085525 DOI: 10.3390/s24092670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/19/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024]
Abstract
Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.
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Affiliation(s)
- Yuanmao Wang
- School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yang Chen
- School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yongjian Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Siyu Liu
- School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China
- Southwest Institute of Technical Physics, Chengdu 610041, China
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Zhou Y, Lin G, Yu X, Cao Y, Cheng H, Shi C, Jiang J, Gao H, Lu F, Shen M. Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images. BIOMEDICAL OPTICS EXPRESS 2023; 14:1848-1861. [PMID: 37206122 PMCID: PMC10191653 DOI: 10.1364/boe.480247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 05/21/2023]
Abstract
The tear fluid reservoir (TFR) under the sclera lens is a unique characteristic providing optical neutralization of any aberrations from corneal irregularities. Anterior segment optical coherence tomography (AS-OCT) has become an important imaging modality for sclera lens fitting and visual rehabilitation therapy in both optometry and ophthalmology. Herein, we aimed to investigate whether deep learning can be used to segment the TFR from healthy and keratoconus eyes, with irregular corneal surfaces, in OCT images. Using AS-OCT, a dataset of 31850 images from 52 healthy and 46 keratoconus eyes, during sclera lens wear, was obtained and labeled with our previously developed algorithm of semi-automatic segmentation. A custom-improved U-shape network architecture with a full-range multi-scale feature-enhanced module (FMFE-Unet) was designed and trained. A hybrid loss function was designed to focus training on the TFR, to tackle the class imbalance problem. The experiments on our database showed an IoU, precision, specificity, and recall of 0.9426, 0.9678, 0.9965, and 0.9731, respectively. Furthermore, FMFE-Unet was found to outperform the other two state-of-the-art methods and ablation models, suggesting its strength in segmenting the TFR under the sclera lens depicted on OCT images. The application of deep learning for TFR segmentation in OCT images provides a powerful tool to assess changes in the dynamic tear film under the sclera lens, improving the efficiency and accuracy of lens fitting, and thus supporting the promotion of sclera lenses in clinical practice.
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Affiliation(s)
- Yuheng Zhou
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Guangqing Lin
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Xiangle Yu
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Yang Cao
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Hongling Cheng
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Ce Shi
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Jun Jiang
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Hebei Gao
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, 325000, China
| | - Fan Lu
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, 325000, China
| | - Meixiao Shen
- Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, 325000, China
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Okuwobi IP, Ding Z, Wan J, Ding S. Artificial intelligence model driven by transfer learning for image-based medical diagnosis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Artificial intelligent (AI) systems for clinical-decision support are an important tool in clinical routine. It has become a crucial diagnostic tool with adequate reliability and interpretability in disease diagnosis and monitoring. Undoubtedly, these models are faced with insufficient data challenges for training, which often directly determines the model’s performance. In order word, insufficient data for model training leads to inefficiency in the model built. To overcome this problem, we propose an AI-driven model by transfer learning in accurate diagnosis for medical decision support. Our approach leverages the shortage of data with a pretrained model by training the neural network with a fraction of the new dataset. For this purpose, we utilized the VGG19 network as the backbone network to support our model in integrating known features with the newly learned features for accurate diagnosis and decision making. Integrating this trained model speeds up the training phase and improve the performance of the proposed model. Experimental results show that the proposed model is effective and efficient in diagnosing different medical diseases. As such, we anticipated that this diagnosis tool will ultimately aid in facilitating early treatment of these treatable diseases, which will improve clinical out-comes.
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Affiliation(s)
- Idowu Paul Okuwobi
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
| | - Zhixiang Ding
- Department of Ophthalmology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Jifeng Wan
- Department of Ophthalmology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Shuxue Ding
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
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