Confident head circumference measurement from ultrasound with real-time feedback for sonographers

Budd S, Sinclair M, Khanal B, Matthew J, Lloyd D, Gomez A, Toussaint N, Robinson EC, Kainz B (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11767 LNCS

Pages Range: 683-691

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322502

DOI: 10.1007/978-3-030-32251-9_75

Abstract

Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose a novel probabilistic Deep Learning approach for real-time automated estimation of fetal HC. This system feeds back statistics on measurement robustness to inform users how confident a deep neural network is in evaluating suitable views acquired during free-hand ultrasound examination. In real-time scenarios, this approach may be exploited to guide operators to scan planes that are as close as possible to the underlying distribution of training images, for the purpose of improving inter-operator consistency. We train on freehand ultrasound data from over 2000 subjects (2848 training/540 test) and show that our method is able to predict HC measurements within 1.81±1.65 mm deviation from the ground truth, with 50% of the test images fully contained within the predicted confidence margins, and an average of 1.82±1.78 mm deviation from the margin for the remaining cases that are not fully contained.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) King’s College London GB United Kingdom (GB)

How to cite

APA:

Budd, S., Sinclair, M., Khanal, B., Matthew, J., Lloyd, D., Gomez, A.,... Kainz, B. (2019). Confident head circumference measurement from ultrasound with real-time feedback for sonographers. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 683-691). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Budd, Samuel, et al. "Confident head circumference measurement from ultrasound with real-time feedback for sonographers." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 683-691.

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