scholarly journals A Hybrid Catheter Localisation Framework in Echocardiography Based on Electromagnetic Tracking and Deep Learning Segmentation

2020 ◽  
Author(s):  
Fei. Jia ◽  
Shu. Wang
Keyword(s):  
Deep Learning ◽  
Medical Device ◽  
Ultrasound Imaging ◽  
Image Method ◽  
Ultrasound Images ◽  
Electromagnetic Tracking ◽  
External Device ◽  
Low Resolution ◽  
Learning Platform ◽  
Catheter Tip

AbstractInterventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases and aortic diseases. Ultrasound imaging, also called echocardiography, is a typical imaging tool that monitors catheter puncturing. Localising a medical device accurately during cardiac interventions can help improve the procedure’s safety and reliability under ultrasound imaging. However, external device tracking and image-based tracking methods can only provide a partial solution. Thus, we proposed a hybrid framework, with the combination of both methods to localise the catheter tip target in an automatic way. The external device used was an electromagnetic tracking system from North Digital Inc (NDI) and the ultrasound image analysis was based on UNet, a deep learning network for semantic segmentation. From the external method, the tip’s location was determined precisely, and the deep learning platform segmented the exact catheter tip automatically. This novel hybrid localisation framework combines the advantages of external electromagnetic (EM) tracking and deep-learning-based image method, which offers a new solution to identify the moving medical device in low-resolution ultrasound images.Featured ApplicationThis framework can be applied to other medical-device localisation fields to help doctors identify a moving target in low-resolution ultrasound images.

scholarly journals Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images

2019 ◽  
Vol 46 (7) ◽  
pp. 3180-3193 ◽  
Author(s):  
Ran Zhou ◽  
Aaron Fenster ◽  
Yujiao Xia ◽  
J. David Spence ◽  
Mingyue Ding
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Ultrasound Images

scholarly journals Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ling-Ping Cen ◽  
Jie Ji ◽  
Jian-Wei Lin ◽  
Si-Tong Ju ◽  
Hong-Jie Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Characteristic Curve ◽  
Weighted Average ◽  
Age Related Macular Degeneration ◽  
Learning Platform ◽  
Age Related ◽  
Public Data ◽  
Primary Test ◽  
Retinal Fundus

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

Efficient Deep Learning Approach to Address Low-Resolution Person Re-Identification

2020 ◽  
Author(s):  
Irfan Yaqoob ◽  
Muhammad Umair Hassan ◽  
Dongmei Niu ◽  
Muhammad Maaz Irfan ◽  
Numan Zafar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Low Resolution

Postnatal ultrasound follow-up in neonates with prenatal hydronephrosis

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Elham Kebriyaei ◽  
Ali Davoodi ◽  
Seyed Alinaghi Kazemi ◽  
Zahra Bazargani
Keyword(s):  
Ultrasound Imaging ◽  
Maternal Education ◽  
Left Kidney ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Renal Anomalies ◽  
Cross Sectional ◽  
Fetal Hydronephrosis ◽  
Kidney Involvement

Abstract Objectives Renal anomalies are the most common fetal abnormalities that occur during prenatal development, and are typically detected by observing hydronephrosis on fetal ultrasound imaging. Follow-up with post-natal ultrasound is important to detect clinically-important obstruction, because many of the pre-natal abnormalities resolve spontaneously. This study aimed to evaluate the postnatal hydronephrosis follow-up rate, and reasons for non follow-up in affected neonates. Methods In this cross-sectional study all neonates born during a period of one year at Ayatollah Mousavi Hospital with hydronephrosis on fetal ultrasound imaging were recruited. All mothers were also given face-to-face information about fetal hydronephrosis and its postnatal outcomes, and follow-up with at least a postnatal ultrasound was recommended from the fourth day of their neonates’ birth until the end of the fourth week. The neonates were subsequently observed for one month to determine the postnatal ultrasound follow-up rate and to reflect on diagnostic test results, reasons for failure to follow-up, as well as causes of hydronephrosis. Results In this study, 71 cases (1.2%) out of 5,952 neonates had fetal hydronephrosis on prenatal ultrasound images. The postnatal ultrasound imaging showed kidney involvement in 18 neonates (25%), particularly in the left kidney (61.1%). Seven neonates had no follow-up at one month (10%). No significant relationship was found between lack of follow-up and the neonates’ place of residence (p=0.42), maternal education (p=0.90), number of siblings (p=0.33), or gender (p=0.64). Conclusions Postnatal ultrasound follow-up rate in these neonates with a history of fetal hydronephrosis was incomplete even though parents had been provided with education and advice at their birth time. Accordingly, it is recommended to perform postnatal ultrasound once neonates are discharged from hospitals.

scholarly journals Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2629
Author(s):  
Kunkyu Lee ◽  
Min Kim ◽  
Changhyun Lim ◽  
Tai-Kyong Song
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Mobile Device ◽  
Network Architecture ◽  
Point Of Care ◽  
Ultrasound Image ◽  
Training Dataset ◽  
Point Of Care Ultrasound ◽  
Reverse Scan ◽  
Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

Cataract detection based on ocular B-ultrasound images by collaborative monitoring deep learning

2021 ◽  
pp. 107442
Author(s):  
Yong Wang ◽  
Chenwei Tang ◽  
Jian Wang ◽  
Yongsheng Sang ◽  
Jiancheng Lv
Keyword(s):  
Deep Learning ◽  
Ultrasound Images
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