Automatic real-time CNN-based neonatal brain ventricles segmentation

Puyang Wang, Nick G. Cuccolo, Rachana Tyagi, Ilker Hacihaliloglu, Vishal M. Patel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Quantitative imaging of brain plays an important role in preterm neonates with a very low birth weight due to the increased risk of developing intraventricular hemorrhage (IVH). In this work, we propose a fully automated method for segmentation of ventricles from two-dimensional (2D) ultrasound (US) scans. The proposed method is based on a Convolutional Neural Network (CNN) that combines the advantages of U-Net and SegNet architectures for ventricles segmentation. Extensive experiments on a dataset consisting of 687 US scans show that the proposed method achieves significant improvements over the state-of-the-art medical image segmentation methods.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages716-719
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Segmentation
  • Ultrasound images
  • Ventricles segmentation

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