Human-level Performance on Automatic Head Biometrics in Fetal Ultrasound Using Fully Convolutional Neural Networks

Sinclair M, Baumgartner CF, Matthew J, Bai W, Martinez JC, Li Y, Smith S, Knight CL, Kainz B, Hajnal J, King AP, Rueckert D (2018)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2018-July

Pages Range: 714-717

Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Event location: Honolulu, HI, USA

ISBN: 9781538636466

DOI: 10.1109/EMBC.2018.8512278

Open Access Link: https://arxiv.org/abs/1804.09102

Abstract

Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model's performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH)

How to cite

APA:

Sinclair, M., Baumgartner, C.F., Matthew, J., Bai, W., Martinez, J.C., Li, Y.,... Rueckert, D. (2018). Human-level Performance on Automatic Head Biometrics in Fetal Ultrasound Using Fully Convolutional Neural Networks. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 714-717). Honolulu, HI, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Sinclair, Matthew, et al. "Human-level Performance on Automatic Head Biometrics in Fetal Ultrasound Using Fully Convolutional Neural Networks." Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA Institute of Electrical and Electronics Engineers Inc., 2018. 714-717.

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