Abstract
Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull strip-ping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual net-work architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.
Original language | English |
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Title of host publication | ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging |
Publisher | IEEE Computer Society |
Pages | 999-1002 |
Number of pages | 4 |
ISBN (Electronic) | 9781538636411 |
DOIs | |
Publication status | Published - 2019 Apr |
Event | 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy Duration: 2019 Apr 8 → 2019 Apr 11 |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2019-April |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Conference
Conference | 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 |
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Country/Territory | Italy |
City | Venice |
Period | 19/4/8 → 19/4/11 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Deep learning
- Infant brain
- Skull stripping
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging