Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
| Autor: | Yuanji Zhang, Haoran Dou, Yi Xiong, Chaoyu Chen, Yuhao Huang, Dong Ni, Jikuan Qian, Xiaoqiong Huang, Rui Li, Wenlong Shi, Xin Yang, Huanjia Luo, Alejandro F. Frangi |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
Computer science Plane (geometry) Process (computing) 030218 nuclear medicine & medical imaging 03 medical and health sciences Transverse plane Software portability 0302 clinical medicine Recurrent neural network medicine Reinforcement learning 3D ultrasound Differentiable function Algorithm 030217 neurology & neurosurgery |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597153 MICCAI (3) |
| DOI: | 10.1007/978-3-030-59716-0_53 |
| Popis: | 3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents’ communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05\(^{\circ }\)/2.21 mm, 8.62\(^{\circ }\)/2.36 mm and 5.93\(^{\circ }\)/0.89 mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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