干旱区生态环境知识资源中心(Arid): Study of multistep Dense U-Net-based automatic segmentation for head MRI scans

Arid

DOI	10.1002/mp.16824
	Study of multistep Dense U-Net-based automatic segmentation for head MRI scans
	Gi, Yongha; Oh, Geon; Jo, Yunhui; Lim, Hyeongjin; Ko, Yousun; Hong, Jinyoung; Lee, Eunjun; Park, Sangmin; Kwak, Taemin; Kim, Sangcheol; Yoon, Myonggeun
通讯作者	Yoon, M
来源期刊	MEDICAL PHYSICS
ISSN	0094-2405
EISSN	2473-4209
出版年	2024
卷号	51 期号:3 页码:2230-2238
英文摘要	BackgroundDespite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used.PurposeThe goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture.MethodsThe MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models.ResultsThe MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter.ConclusionCompared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.
英文关键词	convolution neural network head MRI segmentation MRI histogram standardization skull stripping U-Net
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001100723300001
WOS关键词	CONDUCTIVITY ; MODEL
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404901
推荐引用方式 GB/T 7714	Gi, Yongha,Oh, Geon,Jo, Yunhui,et al. Study of multistep Dense U-Net-based automatic segmentation for head MRI scans[J],2024,51(3):2230-2238.
APA	Gi, Yongha.,Oh, Geon.,Jo, Yunhui.,Lim, Hyeongjin.,Ko, Yousun.,...&Yoon, Myonggeun.(2024).Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.MEDICAL PHYSICS,51(3),2230-2238.
MLA	Gi, Yongha,et al."Study of multistep Dense U-Net-based automatic segmentation for head MRI scans".MEDICAL PHYSICS 51.3(2024):2230-2238.