Automated fetal brain extraction from clinical ultrasound volumes using 3D convolutional neural networks
| Autor: | Ana I. L. Namburete, Ruobing Huang, Bartlomiej W. Papiez, Felipe Moser, Aris T. Papageorghiou |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Modality (human–computer interaction) Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Image processing Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Convolutional neural network Fetal brain Clinical ultrasound FOS: Electrical engineering electronic engineering information engineering Artificial intelligence business |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030393427 |
| Popis: | To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives. 13 pages, 7 figures, MIUA conference |
| Databáze: | OpenAIRE |
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