A sparsity-based atlas selection technique for multiple-atlas segmentation: Application to neonatal brain labeling
- Resource Type
- Conference
- Authors
- Serag, Ahmed; Boardman, James P.; Wilkinson, Alastair Graham; Macnaught, Gillian; Semple, Scott I.
- Source
- 2016 24th Signal Processing and Communication Application Conference (SIU) Signal Processing and Communication Application Conference (SIU), 2016 24th. :2265-2268 May, 2016
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Pediatrics
Image segmentation
Magnetic resonance imaging
Training
Labeling
Distributed databases
Brain
- Language
Quantitative brain tissue volumes from neonatal magnetic resonance imaging (MRI) offer the possibility of improved clinical decision making and diagnosis. However, the neonatal brain presents specific challenges to automated segmentation algorithms. We developed a new method for automatic labeling of neonatal brain MR images. The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain labeling from data of 66 newborns is evaluated and compared with results obtained using majority vote. The proposed method provides accurate brain labeling results with a mean Dice coefficient of 91%. As the proposed method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently.