Searching collaborative agents for multi-plane localization in 3D ultrasound

Huang, Yuhao; Huang, Ruobing; Dou, Haoran; Li, Rui; Qian, Jikuan; Huang, Xiaoqiong; Shi, Wenlong; Chen, Chao‐Yu; Zhang, Yuanji; Wang, Haixia; Xiong, Yi; Ni, Dong

doi:10.1016/j.media.2021.102119

Cited by 23 publications

(17 citation statements)

References 14 publications

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“…As seen in ( Yang et al., 2021a ), the RL-based technique was employed for the first time on 3D US images to localize standard brain planes, including trans -thalamic (TT) and trans -cerebellar (TC) in the second and third trimesters. RL is used in ( Yang et al., 2021b ) to localize the following: mid-sagittal (S), transverse (T), and coronal (C) planes in volumes and trans -thalamic (TT), trans -ventricular (TV), and trans -cerebellar (TC)). Object detection architecture is utilized on 2D US images in ( Lin et al., 2019a ) to localize the trans -thalamic plane in the second trimester of pregnancy, including LS, T, CP, CSP, and third ventricle (TV).…”

Section: Resultsmentioning

confidence: 99%

“…Dueling Deep Q Networks (DDQN) was employed with RNN-based active termination (AT) to identify brain standard planes ( trans -thalamic (TT) and trans -cerebellar (TC)). The second time RL was utilized to monitor fetus health was reported on in a recent study from 2021 ( Yang et al., 2021b ). A multi-agent RL (MARL) was used in a framework that compromises RNN and includes both neural architecture search (NAS) as well as gradient-based differentiable architecture sampler (GDAS).…”

Section: Discussionmentioning

confidence: 99%

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Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

et al. 2022

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“…NAS is also employed to localize multiple uterine standard plan (SP) in 3D Ultrasound (US) simultaneously by Multi-Agent RL (MARL) framework [73]. In MARL, the optimal agent for each plane is obtained by one-shot NAS [26] to avoid time-consuming.…”

Section: Nas For Other Medical Image Applicationsmentioning

confidence: 99%

Meta-Learning of NAS for Few-shot Learning in Medical Image Applications

Vo-Ho¹,

Yamazaki²,

Hoang³

et al. 2022

Preprint

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Deep learning methods have been successful in solving tasks in machine learning and have made breakthroughs in many sectors owing to their ability to automatically extract features from unstructured data. However, their performance relies on manual trialand-error processes for selecting an appropriate network architecture, hyperparameters for training, and pre-/post-procedures. Even though it has been shown that network architecture plays a critical role in learning feature representation feature from data and the final performance, searching for the best network architecture is computationally intensive and heavily relies on researchers' experience. Automated machine learning (AutoML) and its advanced techniques i.e. Neural Architecture Search (NAS) have been promoted to address those limitations. Not only in general computer vision tasks, but NAS has also motivated various applications in multiple areas including medical imaging. In medical imaging, NAS has significant progress in improving the accuracy of image classification, segmentation, reconstruction, and more. However, NAS requires the availability of large annotated data, considerable computation resources, and pre-defined tasks. To address such limitations, meta-learning has been adopted in the scenarios of few-shot learning and multiple tasks. In this book chapter, we first present a brief review of NAS by discussing well-known approaches in search space, search strategy, and evaluation strategy. We then introduce various NAS approaches in medical imaging with different applications such as classification, segmentation, detection, reconstruction, etc. Meta-learning in NAS for few-shot learning and multiple tasks is then explained. Finally, we describe several open problems in NAS.

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“…Most recently, Yeung et al [19] designed an annotation-efficient approach to learn the mapping between the 2D images and 3D space in US. However, all aforementioned methods are limited by the difficulty in optimizing a highly abstract mapping function [18].…”

Section: Introductionmentioning

confidence: 99%

Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

Zou¹,

Dou²,

Huang³

et al. 2022

Preprint

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Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space. Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search. Finally, we propose a spatial-anatomical reward to effectively guide learning trajectories by exploiting spatial and anatomical information simultaneously. We explore the efficacy of our approach on localizing four SPs on uterus and fetal brain datasets. The experiments indicate that our approach achieves a high localization accuracy as well as robust performance.

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Searching collaborative agents for multi-plane localization in 3D ultrasound

Cited by 23 publications

References 14 publications

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

Meta-Learning of NAS for Few-shot Learning in Medical Image Applications

Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

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