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A deep convolutional neural network for brain tissue segmentation in Neonatal MRI

Murphy, Keelin, Boylan, Geraldine B., Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 (2017) A deep convolutional neural network for brain tissue segmentation in Neonatal MRI. In: The 10th International Conference on Brain Monitoring and Neuroprotection in the Newborn, 5-7 Oct 2017, Killarney, Ireland.

Abstract
Brain tissue segmentation is a prerequisite for many subsequent automatic quantitative analysis techniques. As with many medical imaging tasks, a shortage of manually annotated training data is a limiting factor which is not easily overcome, particularly using recent deep-learning technology. We present a deep convolutional neural network (CNN) trained on just 2 publicly available manually annotated volumes, trained to annotate 8 tissue types in neonatal T2 MRI. The network makes use of several recent deep-learning techniques as well as artificial augmentation of the training data, to achieve state-of-the- art results on public challenge data.
Metadata
Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:MRI; brain scans
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:http://newbornbrain2017.com/
Copyright Information:© 2017 the Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant no. SFI 12/RC/2272
ID Code:22062
Deposited On:06 Oct 2017 15:12 by Alan Smeaton . Last Modified 25 Jan 2019 09:45
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