Automated Selection of Standardized Planes from Ultrasound Volume

Rahmatullah, Bahbibi; Papageorghiou, Aris T.; Noble, J. Alison

doi:10.1007/978-3-642-24319-6_5

Cited by 22 publications

(11 citation statements)

References 13 publications

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“…Instead of assisting operators to acquire typical freehand 2D standard planes, previous work has proposed to automatically extract standard planes from data that are acquired with a simplified protocol, such as 3D ultrasound volumes [17,7] or linear sweeps over the maternal abdomen [10]. Moreover, IMUs have been used to acquire 3D ultrasound volumes with 2D probes [5,16].…”

Section: Simplifted Acquisition Protocolsmentioning

confidence: 99%

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Droste

Drukker

Papageorghiou

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the world-wide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8 % for goal prediction and 90.9 % for action prediction.

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Section: Simplifted Acquisition Protocolsmentioning

confidence: 99%

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Droste

Drukker

Papageorghiou

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, the medical experts prepare and follow up on a fetal growth chart based on the biometric measures. A discriminative constrained probabilistic boosting tree (DCPBT) classifier for measuring and identifying the abnormality of the fetus regarding head, abdominal structure, and femur [12][13][14] used the Adaboost method for object detection in 2D fetal abdominal ultrasound images. Kim et al (2018) 15 used an automatic method for estimating fetal biometrics in ultrasound images.…”

Section: Background Studymentioning

confidence: 99%

Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images

Deepika¹,

Suresh²,

Pabitha

2020

Computational Intelligence

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One of the most important industries which protect human from various diseases is the medical industry. Child death is a crucial concern that needs to concentrate on "save the children." Abnormality of a child can be obtained by diagnosing the prenatal by ultrasound system within a specific period for providing better treatment to do "save the children.". This article aimed to diagnose the (prenatal) ultrasound-images by design and implement a novel framework named Defending Against Child Death (DACD). The existing method is a semiautomatic method where it used convolutional neural network (CNN) algorithm for classifying ultrasound images. Real-time medical industry requires a fully automatic method for classifying the ultrasound images to save the human. Hence this article, includes deep learning by implementing five convolutional neural network architectures in an order where it learns, estimate, and confirms the fetus parameters. All the layers in the convolutional neural network extract and classify the different number of features in the ultrasound images automatically and provide a result. The increased number of hidden layers in the CNN can extract even the hidden features of the images. The extracted features are classified automatically and improve the accuracy of 128

show abstract

“…Simplified Acquisition Protocols. Instead of assisting operators to acquire typical freehand 2D standard planes, previous work has proposed to automatically extract standard planes from data that are acquired with a simplified protocol, such as 3D ultrasound volumes [17,7] or linear sweeps over the maternal abdomen [10]. Moreover, IMUs have been used to acquire 3D ultrasound volumes with 2D probes [5,16].…”

Section: Related Workmentioning

confidence: 99%

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Droste¹,

Drukker²,

Papageorghiou³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the worldwide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8 % for goal prediction and 90.9 % for action prediction.

show abstract

Automated Selection of Standardized Planes from Ultrasound Volume

Cited by 22 publications

References 13 publications

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

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