Chen, Q;
Liu, Y;
Hu, Y;
Self, A;
Papageorghiou, A;
Noble, JA;
(2020)
Cross-Device Cross-Anatomy Adaptation Network for Ultrasound Video Analysis.
In:
Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis.
(pp. pp. 42-51).
Springer Nature
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Abstract
Domain adaptation is an active area of current medical image analysis research. In this paper, we present a cross-device and cross-anatomy adaptation network (CCAN) for automatically annotating fetal anomaly ultrasound video. In our approach, deep learning models trained on more widely available expert-acquired and manually-labeled free-hand ultrasound video from a high-end ultrasound machine are adapted to a particular scenario where limited and unlabeled ultrasound videos are collected using a simplified sweep protocol suitable for less-experienced users with a low-cost probe. This unsupervised domain adaptation problem is interesting as there are two domain variations between the datasets: (1) cross-device image appearance variation due to using different transducers; and (2) cross-anatomy variation because the simplified scanning protocol does not necessarily contain standard views seen in typical free-hand scanning video. By introducing a novel structure-aware adversarial training module to learn the cross-device variation, together with a novel selective adaptation module to accommodate cross-anatomy variation domain transfer is achieved. Learning from a dataset of high-end machine clinical video and expert labels, we demonstrate the efficacy of the proposed method in anatomy classification on the unlabeled sweep data acquired using the non-expert and low-cost ultrasound probe protocol. Experimental results show that, when cross-device variations are learned and reduced only, CCAN significantly improves the mean recognition accuracy by 20.8% and 10.0%, compared to a method without domain adaptation and a state-of-the-art adaptation method, respectively. When both the cross-device and cross-anatomy variations are reduced, CCAN improves the mean recognition accuracy by a statistically significant 20% compared with these other state-of-the-art adaptation methods.
| Type: | Proceedings paper |
|---|---|
| Title: | Cross-Device Cross-Anatomy Adaptation Network for Ultrasound Video Analysis |
| Event: | First International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020 |
| ISBN-13: | 978-3-030-60333-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-60334-2_5 |
| Publisher version: | https://doi.org/10.1007/978-3-030-60334-2_5 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115791 |
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