Cited by 32Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and EducationMedical Imaging and Analysis

Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound

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Yang, Xin, Dou, Haoran, Huang, Ruobing, Xue, Wufeng, Huang, Yuhao, Qian, Jikuan, Zhang, Yuanji, Luo, Huanjia +4 more·2021·IEEE Transactions on Medical Imaging

Abstract

Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagnosis. 2D US has to perform scanning for each SP, which is time-consuming and operator-dependent. While 3D US containing multiple SPs in one shot has the inherent advantages of less user-dependency and more efficiency. Automatically locating SP in 3D US is very challenging due to the huge search space and large fetal posture variations. Our previous study proposed a deep reinforcement learning (RL) framework with an alignment module and active termination to localize SPs in 3D US automatically. However, termination of agent search in RL is important and affects the practical deployment. In this study, we enhance our previous RL framework with a newly designed adaptive dynamic termination to enable an early stop for the agent searching, saving at most 67% inference time, thus boosting the accuracy and efficiency of the RL framework at the same time. Besides, we validate the effectiveness and generalizability of our algorithm extensively on our in-house multi-organ datasets containing 433 fetal brain volumes, 519 fetal abdomen volumes, and 683 uterus volumes. Our approach achieves localization error of 2.52mm/10.26°, 2.48mm/10.39°, 2.02mm/10.48°, 2.00mm/14.57°, 2.61mm/9.71°, 3.09mm/9.58°, 1.49mm/7.54°for the transcerebellar, transventricular, transthalamic planes in fetal brain, abdominal plane in fetal abdomen, and mid-sagittal, transverse and coronal planes in uterus, respectively. Experimental results show that our method is general and has the potential to improve the efficiency and standardization of US scanning.

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  1. [32][32] C. J. Watkins and P. Dayan, 'Q-learning,' Machine Learning , vol. 8, no. 3-4, pp. 279-292, 1992. -

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Authors
Yang, X., Dou, H., Huang, R., Xue, W., Huang, Y., Qian, J., Zhang, Y., Luo, H., Guo, H., Wang, T., Xiong, Y., & Ni, D.
Affiliations
Shenzhen University (CN)
Venue
IEEE Transactions on Medical Imaging
Published
2021-07
Volume
40
Issue
7
Pages
1950-1961
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
OpenAlex
W3142022860
Cited by
32
References
53
Citations by year
2026: 5 · 2025: 10 · 2024: 3 · 2023: 8 · 2022: 6
FWCI
1.53
Type
journal article
Language
en
Open access
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Research Explorer (The University of Manchester) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Key Research and Development Program of China (2019YFC0118300)
  • Shenzhen Peacock Plan (KQTD2016053112051497, KQJSCX20180328095606003)
  • Natural Science Foundation of Shenzhen (JCYJ20190808115419619)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
3D ultrasound, warm start, deep reinforcement learning, prenatal ultrasound, fetal brain, uterus, inference efficiency
MeSH terms
Abdomen, Algorithms, Female, Humans, Pregnancy, Ultrasonography, Ultrasonography, Prenatal, Imaging, Three-Dimensional

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