Paper Info
Title
Simultaneous Tissue Classification And Lateral Ventricle Segmentation Via A 2d U-Net Driven By A 3d Fully Convolutional Neural Network
Abstract
In this paper, we proposed and validated a novel and fully automatic pipeline for simultaneous tissue classification and lateral ventricle segmentation via a 2D U-net. The 2D U-net was driven by a 3D fully convolutional neural network (FCN). Multiple T1-weighted atlases which had been pre-segmented into six whole-brain regions including the gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), lateral ventricles (LVs), skull, and the background of the entire image were used. In the proposed pipeline, probability maps of the six whole-brain regions of interest (ROIs) were obtained after a pre-segmentation through a trained 3D patch-based FCN. To further capture the global context of the entire image, the to-be-segmented image and the corresponding six probability maps were input to a trained 2D U-net in a 2D slice fashion to obtain the final segmentation map. Experiments were performed on a dataset consisting of 18 T1-weighted images. Compared to the 3D patch-based FCN on segmenting five ROIs (GM, WM, CSF, LVs, skull) and another two classical methods (SPM and FSL) on segmenting GM and WM, the proposed pipeline showed a superior segmentation performance. The proposed segmentation architecture can also be extended to other medical image segmentation tasks.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856668
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Lateral ventricles,Computer vision,Convolutional neural network,Segmentation,Computer science,Image segmentation,Ventricle,Artificial intelligence
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jiong Wu123.06
Yue Zhang201.35
Xiaoying Tang388.79