Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data

Nie, Siqing; Yu, Jinhua; Chen, Ping; Wang, Yuanyuan; Zhang, Jian Qiu

doi:10.1016/j.ultrasmedbio.2016.08.034

Cited by 28 publications

(20 citation statements)

References 23 publications

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“…Moreover, [115] presented a modified DBN technique to minimize the computation load from 3D ultrasound data for the first trimester of pregnancy, which is an important parameter in prenatal screening. The technique converts the sagittal plane into a symmetry plane and axis searching problem.…”

Section: Review Of Deep Learning Implementation In Health Carementioning

confidence: 99%

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Igbe

Liu

et al. 2019

JMIR Mhealth Uhealth

134

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The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

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Section: Review Of Deep Learning Implementation In Health Carementioning

confidence: 99%

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Igbe

Liu

et al. 2019

JMIR Mhealth Uhealth

134

View full text Add to dashboard Cite

show abstract

“…In (11), the information extracted from both cropped regions of fetal structures and the whole ultrasound image via CNN are suggested to be fused in order to identify fetal standard planes. Besides, a few works have been carried out for the localization of fetal standard planes in 3-D ultrasound volumes (12,13).…”

Section: Related Workmentioning

confidence: 99%

A Deep Convolutional Neural Network based Hybrid Framework for Fetal Head Standard Plane Identification

Ye¹,

Zou²,

Zhang³

et al. 2020

Preprint

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As considered to be less risky, less expensive, and more convenient than radiological examinations, ultrasound has been routinely employed in prenatal exams for the past decades. However, the quality of acquired ultrasound samples, i.e., ultrasound images or videos, and the further diagnosis is crucially depended on the sonographer. At the meantime, there are an extremely limited number of experienced sonographer available for the fetal ultrasound screening. Therefore, to reduce the workload of sonographers, and to promote the quality of fetal ultrasound screening, a deep convolutional neural network based framework is proposed for automatically differentiating five types of fetal head ultrasound standard planes, i.e., Transventricular plane (TV), Transthalamic plane (TT), Transcerebellar plane (TC), Coronal view of eyes (Eyes), Coronal view of nose (Nose), and other non-standard fetal head ultrasound images (Background). A dataset consists of 19928 fetal ultrasound images is applied for the model training and performance evaluation. By combining object detection network, object classification network, and model stacking technique, the proposed framework achieves the state-of-the-art performance with the average accuracy of 89.61% and the average F-1 score of 89.61%.

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“…The other applications include the quality control of fetal ultrasound and standard views of the fetus [ 12 ], and the segmentations of non-rigid organ [ 13 ] and rigid organ [ 14 ]. Three-dimensional analysis has not been widely used because of expensive calculations and limited datasets [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

et al. 2021

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In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac views is proposed consisting of three processes. First, a spatial transform network is performed to learn cardiac shape changes during a cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrate channel-wise feature responses. Finally, the structured signals by the similarities among cardiac views are transformed into the graph-based image embedding, which acts as unsupervised regularization constraints to improve the generalization accuracy. The proposed method is trained and tested in 171792 cardiac images from 584 subjects. The overall accuracy of the proposed method on cardiac image classification is 99.10%, and the mean AUC is 99.36%, better than known methods. Moreover, the overall accuracy is 97.73%, and the mean AUC is 98.59% on an independent test set with 37,883 images from 100 subjects. The proposed automated recognition model achieved comparable accuracy with true cardiac views, and thus can be applied clinically to help find standard cardiac views.

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Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data

Cited by 28 publications

References 23 publications

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

A Deep Convolutional Neural Network based Hybrid Framework for Fetal Head Standard Plane Identification

Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

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