SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

By Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, Daniel Rueckert. 2016

388 citations recorded by the source corpus.

Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

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): “the prediction with respect to an input image [27, 5, 54] or analysing the activations of the feature maps for the image [65, 41, 43] to determine which part of the image was responsible for making the associated prediction.”
Automatic Shadow Detection in 2D Ultrasound (2018, methodology): “The classifier we use has a similar architecture to SonoNet-32 [6], which performs well for 2D ultrasound fetal standard view classification and this classifier provides soft predictions p(xi|l = 1) for image xi during testing.”
Metrics Reloaded: Recommendations for image analysis validation (2022, neutral): “(4) Diagnostic standard plane classification in ultrasound images [9].”
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,…”