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DOI10.1109/TMI.2017.2721362
Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
Salehi, Seyed Sadegh Mohseni1,2,3; Erdogmus, Deniz1; Gholipour, Ali2,3
通讯作者Salehi, Seyed Sadegh Mohseni
来源期刊IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
EISSN1558-254X
出版年2017
卷号36期号:11页码:2319-2330
英文摘要

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.


英文关键词Brain extraction whole brain segmentation MRI convolutional neural network CNN U-net auto-context
类型Article
语种英语
国家USA
收录类别SCI-E
WOS记录号WOS:000414134200012
WOS关键词AUTOMATIC SEGMENTATION ; VOLUME RECONSTRUCTION ; TUMOR SEGMENTATION ; MRI
WOS类目Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; 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/2XILL650/199587
作者单位1.Northeastern Univ, Elect & Comp Engn Dept, Boston, MA 02115 USA;
2.Boston Childrens Hosp, Radiol Dept, Boston, MA 02115 USA;
3.Harvard Med Sch, Boston, MA 02115 USA
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GB/T 7714
Salehi, Seyed Sadegh Mohseni,Erdogmus, Deniz,Gholipour, Ali. Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging[J],2017,36(11):2319-2330.
APA Salehi, Seyed Sadegh Mohseni,Erdogmus, Deniz,&Gholipour, Ali.(2017).Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.IEEE TRANSACTIONS ON MEDICAL IMAGING,36(11),2319-2330.
MLA Salehi, Seyed Sadegh Mohseni,et al."Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging".IEEE TRANSACTIONS ON MEDICAL IMAGING 36.11(2017):2319-2330.
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