Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

Autor: Ruobing Huang, Yi Xiong, Xiaoqiong Huang, Yuhao Huang, Haoran Dou, Jikuan Qian, Rui Li, Chaoyu Chen, Wenlong Shi, Yuanji Zhang, Dong Ni, Xin Yang, Haixia Wang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Health Informatics
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Imaging
Three-Dimensional
medicine
FOS: Electrical engineering
electronic engineering
information engineering
Reinforcement learning
Humans
Computer Science - Multiagent Systems
Radiology
Nuclear Medicine and imaging
3D ultrasound
Differentiable function
Electrical Engineering and Systems Science - Signal Processing
Ultrasonography
Radiological and Ultrasound Technology
medicine.diagnostic_test
Plane (geometry)
Image and Video Processing (eess.IV)
Uterus
Process (computing)
Electrical Engineering and Systems Science - Image and Video Processing
Computer Graphics and Computer-Aided Design
Transverse plane
Recurrent neural network
Coronal plane
Female
Computer Vision and Pattern Recognition
Neural Networks
Computer
Algorithm
030217 neurology & neurosurgery
Multiagent Systems (cs.MA)
Popis: 3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one shot. Thus, automatically localizing SPs in 3D US has the potential to improve user-independence and scanning-efficiency. However, manual SP localization in 3D US is challenging because of the low image quality, huge search space and large anatomical variability. In this work, we propose a novel multi-agent reinforcement learning (MARL) framework to simultaneously localize multiple SPs in 3D US. Our contribution is four-fold. First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets. Second, we equip the MARL system with a recurrent neural network (RNN) based collaborative module, which can strengthen the communication among agents and learn the spatial relationship among planes effectively. Third, we explore to adopt the neural architecture search (NAS) to automatically design the network architecture of both the agents and the collaborative module. Last, we believe we are the first to realize automatic SP localization in pelvic US volumes, and note that our approach can handle both normal and abnormal uterus cases. Extensively validated on two challenging datasets of the uterus and fetal brain, our proposed method achieves the average localization accuracy of 7.03 degrees/1.59mm and 9.75 degrees/1.19mm. Experimental results show that our light-weight MARL model has higher accuracy than state-of-the-art methods.
Accepted by Medical Image Analysis (10 figures, 8 tabels)
Databáze: OpenAIRE
Externí odkaz: