Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation

doi:10.1007/978-3-031-72069-7_23

	Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation
	Teng, Lin1 ; Zhao, Zihao1 ; Huang, Jiawei1 ; Cao, Zehong 2; Meng, Runqi1 ; Shi, Feng 2; Shen, Dinggang1,2,3
	2024
会议录名称	MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT II (IF:0.402[JCR-2005],0.000[5-Year])
ISSN	0302-9743
卷号	15002
发表状态	已发表
DOI	10.1007/978-3-031-72069-7_23
摘要	Automatic and accurate segmentation of brain MR images throughout the human lifespan into tissue and structure is crucial for understanding brain development and diagnosing diseases. However, challenges arise from the intricate variations in brain appearance due to rapid early brain development, aging, and disorders, compounded by the limited availability of manually-labeled datasets. In response, we present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels, followed by the incorporation of knowledge-driven embeddings learned from image-text alignment into the models. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes, enabling models to learn structural feature embeddings across diverse age groups. Experimental findings demonstrate the superiority and robustness of our proposed method, particularly noticeable when employing Swin UNETR as the backbone. Our approach achieves average DSC values of 95.17% and 94.19% for brain tissue and structure segmentation, respectively. Our code is available at https://github.com/TL9792/KGPL.
关键词	Brain MRI segmentation Across the lifespan Knowledge-guided prompt learning Fine tuning Transfer learning
会议名称	27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点	Palmeraie Conf Ctr,Marrakesh,MOROCCO
会议日期	OCT 06-10, 2024
URL	查看原文
收录类别	CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China["62131015","62250710165","U23A20295"] ; STI 2030Major Projects[2022ZD0209000] ; Shanghai Municipal Central Guided Local Science and Technology Development Fund[YDZX20233100001001] ; Science and Technology Commission of Shanghai Municipality (STCSM)[21010502600] ; Key R&D Program of Guangdong Province, China["2023B0303040001","2021B0101420006"]
WOS研究方向	Computer Science ; Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001342225800023
出版者	SPRINGER INTERNATIONAL PUBLISHING AG
EISSN	1611-3349
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/421325
专题	生物医学工程学院_PI研究组_沈定刚组信息科学与技术学院_硕士生信息科学与技术学院_博士生生物医学工程学院_博士生
通讯作者	Shi, Feng; Shen, Dinggang
作者单位	1.School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China 2.Shanghai United Imaging Intelligence Co., Ltd. Shanghai 200230, China 3.Shanghai Clinical Research and Trial Center, Shanghai 201210, China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Teng, Lin,Zhao, Zihao,Huang, Jiawei,et al. Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024.