TY - GEN
T1 - USPoint
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
AU - Zhao, Cheng
AU - Droste, Richard
AU - Drukker, Lior
AU - Papageorghiou, Aris T.
AU - Noble, J. Alison
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Ultrasound (US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regression-based methods leverage the DNN to overfit on the specific training data, which is naturally lack of generalization ability for the clinical application. In this paper, we are back to generalized US feature learning rather than deep parameter regression. We propose a self-supervised learned local detector and descriptor, named USPoint, for US-probe motion estimation during the fine-adjustment phase of fetal plane acquisition. Specifically, a hybrid neural architecture is designed to simultaneously extract a local feature, and further estimate the probe motion. By embedding a differentiable USPoint-based motion estimation inside the proposed network architecture, the USPoint learns the keypoint detector, scores and descriptors from motion error alone, which doesn’t require expensive human-annotation of local features. The two tasks, local feature learning and motion estimation, are jointly learned in a unified framework to enable collaborative learning with the aim of mutual benefit. To the best of our knowledge, it is the first learned local detector and descriptor tailored for the US image. Experimental evaluation on real clinical data demonstrates the resultant performance improvement on feature matching and motion estimation for potential clinical value. A video demo can be found online: https://youtu.be/JGzHuTQVlBs.
AB - Ultrasound (US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regression-based methods leverage the DNN to overfit on the specific training data, which is naturally lack of generalization ability for the clinical application. In this paper, we are back to generalized US feature learning rather than deep parameter regression. We propose a self-supervised learned local detector and descriptor, named USPoint, for US-probe motion estimation during the fine-adjustment phase of fetal plane acquisition. Specifically, a hybrid neural architecture is designed to simultaneously extract a local feature, and further estimate the probe motion. By embedding a differentiable USPoint-based motion estimation inside the proposed network architecture, the USPoint learns the keypoint detector, scores and descriptors from motion error alone, which doesn’t require expensive human-annotation of local features. The two tasks, local feature learning and motion estimation, are jointly learned in a unified framework to enable collaborative learning with the aim of mutual benefit. To the best of our knowledge, it is the first learned local detector and descriptor tailored for the US image. Experimental evaluation on real clinical data demonstrates the resultant performance improvement on feature matching and motion estimation for potential clinical value. A video demo can be found online: https://youtu.be/JGzHuTQVlBs.
KW - Local detector and descriptor
KW - Obstetric US
KW - Probe motion
UR - http://www.scopus.com/inward/record.url?scp=85139019271&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16449-1_11
DO - 10.1007/978-3-031-16449-1_11
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
C2 - 37223131
AN - SCOPUS:85139019271
SN - 9783031164484
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 114
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2022 through 22 September 2022
ER -