Learning Ultrasound Imaging (Learning Imaging)

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  • Introduction;
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  • Deep Learning in Medical Ultrasound Analysis: A Review - ScienceDirect.

They used a convolutional neural network CNN to determine the stomach bubble, amniotic fluid and umbilical vein in the ultrasound image, and from these three features, derived an estimate of the AC. This information was then used to refine the initial AC estimate.

In the final step, the researchers passed the final AC measurement along with the ultrasound images to a specific class of CNN known as a U-net. The U-net decided whether or not the ultrasound images, together with the AC estimate, are accepted or rejected, in a manner that mimics the decision of the clinician. In this way, the machine learns what to look for.

Kim and colleagues used images to train each CNN and the U-net, and 62 images to evaluate abdominal circumference. They obtained accurate segmentation of the ultrasound images, including images rejected by the machine because they showed the wrong anatomical plane, in Commercial systems are available to estimate the abdominal cavity volume from ultrasound images.

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However, these methods often fall short of clinical requirement due their inability to utilize structural information within the ultrasound image, such as shadowing artefacts caused by the ribs. By using a combination of several CNNs and a U-net, Kim and colleagues have shown that machines have the capacity to learn how to provide this structural information, which can in turn assist clinicians by automatically segmenting images with a good degree of accuracy.

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  1. Learning Ultrasound Imaging | Jose Luís del Cura | Springer.
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  5. Diagnostic imaging Research update Machine learning identifies foetal size from ultrasound images 20 Nov Liam Stubbington. Deep learning-based measurement Deep learning-based abdominal circumference AC measurement: input image and initial AC estimation process top row ; AC measuring procedure middle row ; acceptance check and output bottom row.

    Substituting clinicians with a machine-learning algorithm The researchers used a three-stage approach. The research team from Yonsei University. Read more. Most commonly asked questions about FutureLearn. Get personalized course recommendations, track subjects and courses with reminders, and more. Read Close. University of Twente via FutureLearn 3.

    Ultrasound Hands-on Training – Interactive Course on Venous Ultrasound Imaging

    Add to list. Found in Health Care. Go to class. Become a Data Scientist datacamp. Start now for free! Sign up. Overview Ultrasound is an easy and quick technique to visualize structures inside the human body. Understand ultrasound imaging from three perspectives This free online course is based on three major perspectives: Medical perspective: In Week 1 we will examine the versatile use of ultrasound imaging for medical purposes.

    Technical perspective: In Weeks 2 to 4 we will explain the underlying technology of ultrasound imaging.

    Technical medical perspective: In Weeks 5 and 6 we will apply your newly acquired technical background knowledge of ultrasound to make better images. Taught by Jordy van Zandwijk and Wiendelt Steenbergen. Tags europe ultrasound imaging. Browse More FutureLearn Articles.

    Deep learning in ultrasound imaging

    Browse More Health Care courses. It is very interesting and useful course as for beginners and also for professional sonologists , because this kind of courses helps to improve knowledges of beginners and helps prefessional sonografists repeat forgotten ultrasonologic skills. Great Thank for everyone who prepared this course.