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Monkam P, Lu W, Jin S, Shan W, Wu J, Zhou X, Tang B, Zhao H, Zhang H, Ding X, Chen H, Su L. US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images. Comput Biol Med 2023; 152:106385. [PMID: 36493732 DOI: 10.1016/j.compbiomed.2022.106385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/21/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis. METHODS We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set. RESULTS Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy. CONCLUSIONS The proposed framework can help improve the accuracy of ultrasound diagnosis.
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Affiliation(s)
- Patrice Monkam
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Wenkai Lu
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Songbai Jin
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Wenjun Shan
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Jing Wu
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Xiang Zhou
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Bo Tang
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Hua Zhao
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Hongmin Zhang
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Xin Ding
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Huan Chen
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Longxiang Su
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
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Moinuddin M, Khan S, Alsaggaf AU, Abdulaal MJ, Al-Saggaf UM, Ye JC. Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network. Front Physiol 2022; 13:961571. [DOI: 10.3389/fphys.2022.961571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 10/19/2022] [Indexed: 11/16/2022] Open
Abstract
Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.
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Wang Q, Liu D, Liu G. Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4010339. [PMID: 35035520 PMCID: PMC8759876 DOI: 10.1155/2022/4010339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/28/2021] [Accepted: 12/08/2021] [Indexed: 11/30/2022]
Abstract
This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and F1-score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant (P < 0.05). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid (P < 0.05). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.
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Affiliation(s)
- Qiang Wang
- Department of Ultrasonography, Yidu Central Hospital of Weifang, Qingzhou, 262500 Shandong, China
| | - Dong Liu
- Department of Ultrasonography, Yidu Central Hospital of Weifang, Qingzhou, 262500 Shandong, China
| | - Guangheng Liu
- Department of Ultrasonography, Weifang People's Hospital, Weifang, 261041 Shandong, China
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