Paper Info
Title
Learning-Based Multi-atlas Segmentation of the Lungs and Lobes in Proton MR Images.
Abstract
Delineation of the lung and lobar anatomy in MR images is challenging due to the limited image contrast and the absence of visible interlobar fissures. Here we propose a novel automated lung and lobe segmentation method for pulmonary MR images. This segmentation method employs prior information of the lungs and lobes extracted from CT in the form of multiple MRI atlases, and adopts a learning-based atlas-encoding scheme, based on random forests, to improve the performance of multi-atlas segmentation. In particular, we encode each CT-derived MRI atlas by training an atlas-specific random forest for each structure of interest. In addition to appearance features, we also extract label context features from the registered atlases to introduce additional information to the non-linear mapping process. We evaluated our proposed framework on 10 clinical MR images acquired from COPD patients. It outperformed state-of-the-art approaches in segmenting the lungs and lobes, yielding a mean Dice score of 95.7%.
Year
Venue
Field
2017
MICCAI
Computer vision,Pattern recognition,Computer science,Segmentation,Lobe,Atlas (anatomy),Artificial intelligence,Random forest,Interlobar
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
12
4
Name
Order
Citations
PageRank
Hoileong Lee100.34
Tahreema N. Matin236416.26
F. Gleeson324214.91
Vicente Grau43812.23