Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach.
Cardiologists can acquire important information related to patients' cardiac health using carotid artery stiffness, its lumen diameter (LD), and its carotid intima-media thickness (cIMT). The sonographers primarily concern about the location of the artery in B-mode ultrasound images. Localization us...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 3; pp. 471 - 483 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142105116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142105116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2020 vid: 58 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142105116 142105116 NLM31897798 10.1007/s11517-019-02099-3 NLM31897798 142105116 ppf: 471 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach. aug: au: Jain, Pankaj K. Gupta, Saurabh Bhavsar, Arnav Nigam, Aditya Sharma, Neeraj affil: Indian Institute of Technology Varanasi, Banaras Hindu University, Varanasi, UP, India sug: subj: Ultrasonography Carotid Arteries Benchmarking Reproducibility of Results Image Processing, Computer Assisted Algorithms Clinical Assessment Tools Scales ab: Cardiologists can acquire important information related to patients' cardiac health using carotid artery stiffness, its lumen diameter (LD), and its carotid intima-media thickness (cIMT). The sonographers primarily concern about the location of the artery in B-mode ultrasound images. Localization using manual methods is tedious and time-consuming and also may lead to some errors. On the other hand, automated approaches are more objective and can provide the localization of the artery at near real time. Above arterial parameters may be determined after localization of the artery in real time.A novel method of localization of common carotid artery (CCA) transverse section is presented in this work. The method is known as fast region convolutional neural network (FRCNN)-based localization method and is designed using a stack of three layers viz. convolutional layers, fully connected layers, and pooling layers. These organized layers constitute a region proposal network (RPN) and an object class detection network (OCDN). We obtain an outcome as a bounding box along with a score of prediction around the cross-section of the CCA.B-mode ultrasound image database of CCA is split into training and testing set, to accomplish this, three partition methods K = 2, 5, and 10 are used in our work. The training is extended for 30, 200, and 2000 epochs in order to achieve fine-tuned features from the convolutional neural network. After 2000 epochs, we obtain 95% validation accuracy; however, mean of the accuracies up to 2000 epochs is 89.36% for K = 10 partitions protocol (training 90%, testing 10%). Generated CNN model is tested on a different dataset of 433 images and the acquired accuracy is 87.99%. Thus, the proposed method including an advanced deep learning technique demonstrates promising localization for carotid artery transverse section. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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