Multi-Atlas Segmentation with Joint Label Fusion and Corrective Learning - An Open Source Implementation
Label fusion based multi-atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. This technique transfers segmentations from expert-labeled images, called atlases, to a novel image using deformable image registration. Errors produced by label transf...
Main Authors: | Hongzhi eWang, Paul eYushkevich |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2013-11-01
|
Series: | Frontiers in Neuroinformatics |
Subjects: | |
Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00027/full |
Similar Items
-
Multi-atlas label fusion by using supervised local weighting for brain image segmentation
by: D. Cárdenas-Peña, et al.
Published: (2017-05-01) -
Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline
by: Jiahui eWang, et al.
Published: (2014-02-01) -
Personalized Knee Geometry Modeling Based on Multi-Atlas Segmentation and Mesh Refinement
by: Filippos P. Nikolopoulos, et al.
Published: (2020-01-01) -
Preliminary Analysis Using Multi-atlas Labeling Algorithms for Tracing Longitudinal Change
by: Eun Young eKim, et al.
Published: (2015-07-01) -
Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation
by: Qiang Zheng, et al.
Published: (2018-10-01)