干旱区生态环境知识资源中心(Arid): Brain MR image simulation for deep learning based medical image analysis networks

Arid

DOI	10.1016/j.cmpb.2024.108115
	Brain MR image simulation for deep learning based medical image analysis networks
	Ayaz, Aymen; Al Khalil, Yasmina; Amirrajab, Sina; Lorenz, Cristian; Weese, Juergen; Pluim, Josien; Breeuwer, Marcel
通讯作者	Ayaz, A
来源期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
EISSN	1872-7565
出版年	2024
卷号	248
英文摘要	Background and Objective: As large sets of annotated MRI data are needed for training and validating deep learning based medical image analysis algorithms, the lack of sufficient annotated data is a critical problem. A possible solution is the generation of artificial data by means of physics -based simulations. Existing brain simulation data is limited in terms of anatomical models, tissue classes, fixed tissue characteristics, MR sequences and overall realism. Methods: We propose a realistic simulation framework by incorporating patient -specific phantoms and Bloch equations -based analytical solutions for fast and accurate MRI simulations. A large number of labels are derived from open -source high -resolution T1w MRI data using a fully automated brain classification tool. The brain labels are taken as ground truth (GT) on which MR images are simulated using our framework. Moreover, we demonstrate that the T1w MR images generated from our framework along with GT annotations can be utilized directly to train a 3D brain segmentation network. To evaluate our model further on larger set of real multi -source MRI data without GT, we compared our model to existing brain segmentation tools, FSL-FAST and SynthSeg. Results: Our framework generates 3D brain MRI for variable anatomy, sequence, contrast, SNR and resolution. The brain segmentation network for WM/GM/CSF trained only on T1w simulated data shows promising results on real MRI data from MRBrainS18 challenge dataset with a Dice scores of 0 .818/0 .832/0 .828. On OASIS data, our model exhibits a close performance to FSL, both qualitatively and quantitatively with a Dice scores of 0 .901/0 .939/0 .937. Conclusions: Our proposed simulation framework is the initial step towards achieving truly physics -based MRI image generation, providing flexibility to generate large sets of variable MRI data for desired anatomy, sequence, contrast, SNR, and resolution. Furthermore, the generated images can effectively train 3D brain segmentation networks, mitigating the reliance on real 3D annotated data.
英文关键词	Brain MRI simulation Large synthetic population Brain MRI segmentation WM/GM/CSF segmentation
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001217400500001
WOS关键词	SEGMENTATION ; VALIDATION ; PROJECT ; MODEL
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403246
推荐引用方式 GB/T 7714	Ayaz, Aymen,Al Khalil, Yasmina,Amirrajab, Sina,et al. Brain MR image simulation for deep learning based medical image analysis networks[J],2024,248.
APA	Ayaz, Aymen.,Al Khalil, Yasmina.,Amirrajab, Sina.,Lorenz, Cristian.,Weese, Juergen.,...&Breeuwer, Marcel.(2024).Brain MR image simulation for deep learning based medical image analysis networks.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,248.
MLA	Ayaz, Aymen,et al."Brain MR image simulation for deep learning based medical image analysis networks".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 248(2024).