Efficient Medical Image Segmentation via Reinforcement Learning-Driven K-Space Sampling

doi:10.1109/TETCI.2025.3621221

CSpace

	Efficient Medical Image Segmentation via Reinforcement Learning-Driven K-Space Sampling
	Li, Yuqi 1; Zeng, Hansheng 2; Zhang, Fuyan 1; Yang, Chuanguang 1; Li, Yanli 3,4; Ding, Weiping 3,5
	2025-11-11
发表期刊	IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
ISSN	2471-285X
页码	14
摘要	Magnetic Resonance Imaging (MRI) excels in medical diagnostics with its superior soft tissue contrast and detailed anatomical visualization, providing critical support for precise medical image segmentation. However, traditional full K-space sampling is time-consuming, limiting efficiency in clinical settings. To address this challenge, we propose a novel method leveraging Reinforcement Learning (RL) to adaptively sample K-space, optimizing both MRI acquisition efficiency and segmentation accuracy. Our approach features an RL-driven policy network that strategically selects the most informative K-space samples, substantially reducing scan times while maintaining critical anatomical details. By integrating segmentation performance into the reward model, our method directly aligns the sampling process with accurate pathological segmentation. Furthermore, sparse K-space data are reconstructed into high-quality images, ensuring precise inputs for segmentation networks. Experiments on ACDC, AMOS, M&Ms-2, CHAOS and MSD datasets demonstrate that our approach not only accelerates MRI processing but also significantly enhances segmentation accuracy, showcasing its potential for clinical applications where speed and precision are paramount.
关键词	Magnetic resonance imaging Image reconstruction Image segmentation Accuracy Reinforcement learning Pathology Medical diagnostic imaging Adaptation models Measurement Computational intelligence k-space sampling magnetic resonance imaging reinforcement learning
DOI	10.1109/TETCI.2025.3621221
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001616328500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/43066
专题	中国科学院计算技术研究所
通讯作者	Ding, Weiping
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Hong Kong, Pokfulam, Hong Kong, Peoples R China 3.Nantong Univ, Sch Artificial Intelligence & Comp Sci, Nantong 226007, Peoples R China 4.Univ Sydney, Camperdown, NSW 2050, Australia 5.City Univ Macau, Fac Data Sci, Macau 999078, Peoples R China
推荐引用方式 GB/T 7714	Li, Yuqi,Zeng, Hansheng,Zhang, Fuyan,et al. Efficient Medical Image Segmentation via Reinforcement Learning-Driven K-Space Sampling[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,2025:14.
APA	Li, Yuqi,Zeng, Hansheng,Zhang, Fuyan,Yang, Chuanguang,Li, Yanli,&Ding, Weiping.(2025).Efficient Medical Image Segmentation via Reinforcement Learning-Driven K-Space Sampling.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,14.
MLA	Li, Yuqi,et al."Efficient Medical Image Segmentation via Reinforcement Learning-Driven K-Space Sampling".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2025):14.