FULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION.
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.
Department of Computer Science, UNC-Chapel Hill; Department of Radiology and BRIC, UNC-Chapel Hill.
Dong
D
Nie
Department of Radiology and BRIC, UNC-Chapel Hill.
Li
L
Wang
Department of Computer Science, UNC-Chapel Hill; Department of Radiology and BRIC, UNC-Chapel Hill.
Yaozong
Y
Gao
Department of Radiology and BRIC, UNC-Chapel Hill.
Dinggang
D
Shen
FCN; brain image; multi-modality; segmentation
J Neurosci Methods. 2013 Jan 15;212(1):43-55
23032117
Med Image Anal. 2000 Mar;4(1):43-55
10972320
Neuroimage. 2009 Aug 15;47(2):564-72
19409502
Med Image Anal. 2012 Dec;16(8):1565-79
22921305
Med Image Anal. 2005 Oct;9(5):457-66
16019252
Neuroimage. 2014 Apr 1;89:152-64
24291615
Neuroimage. 2007 Nov 15;38(3):461-77
17888685
PLoS One. 2012;7(9):e44596
23049751
Neuroimage. 2015 Mar;108:160-72
25541188
Neuroimage. 2011 Oct 1;58(3):805-17
21763443
IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651
27244717
Neuroimage. 2015 Mar;108:214-24
25562829
Neuroimage. 2014 Jan 1;84:141-58
23968736