Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks

By Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Sandra Smith, Bernhard Kainz, Daniel Rueckert. 2016

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pediatrics, Perinatology and Child Health

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In Artificial Intelligence

In Pediatrics, Perinatology and Child Health

Daniel Rueckert

Citations in the corpus

Visual Feature Attribution Using Wasserstein GANs (2017, methodology): “[5, 6] use a variant of [53] to localise fetal anatomy.”
A survey on deep learning in medical image analysis (2017, methodology): “Baumgartner et al. (2016) trained CNNs on video frame data to detect up to 12 standardized scan planes in mid-pregnancy”
Detection and Characterization of the Fetal Heartbeat in Free-hand Ultrasound Sweeps with Weakly-supervised Two-streams Convolutional Networks (2017, methodology): “[4] employed a fully convolutional network to detect 12 standard planes and localise the respective fetal anatomy.”
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound (2016, methodology): “In our preliminary work [2], we backpropagated the error only from a fixed percentile P of the most highly activated neurons in Fk to achieve a localisation effect.”
A review on deep learning in medical image analysis (2021, methodology): “These autoen-coder models are educated by providing the input as both input and target output, forcing the model to reproduce the input by first encoding it to a compressed representation and then decoding it back to the original [53].”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “For the same standard plane detection task in fetal ultrasound, Baum-gartner et al. 137 proposed a joint CNN framework to classify 12 standard scan planes and also localize the fetal anatomy using a series of ultrasound fetal midpregnancy scans.”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “The work in Baumgartner et al. (2016) developed a model that helps to localize the fetal in the image.”
Deep Learning in Medical Ultrasound Analysis: A Review (2019, background): “Baumgartner et al. [117,118] and Chen et al. [78,119] accomplished the detection of 13 fetal standard views (e.g., kidneys, brain, abdominal, spine, femur, and cardiac plane) and the fetal abdominal (or face and four-chamber view) standard plane in 2D US images through the transferred deep models,…”