ICHNet: Intracerebral hemorrhage (ICH) segmentation using deep learning

Published

Conference Paper

© Springer Nature Switzerland AG 2019. We develop a deep learning approach for automated intracerebral hemorrhage (ICH) segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by integrating dilated convolution neural network (CNN) with hypercolumn features where a modest number of pixels are sampled and corresponding features from multiple layers are concatenated. Due to freedom of sampling pixels rather than image patch, this model trains within the brain region and ignores the CT background padding. This boosts the convergence time and accuracy by learning only healthy and defected brain tissues. To overcome the class imbalance problem, we sample an equal number of pixels from each class. We also incorporate 3D conditional random field (3D CRF) to smoothen the predicted segmentation as a post-processing step. ICHNet demonstrates 87.6% Dice accuracy in hemorrhage segmentation, that is comparable to radiologists.

Full Text

Duke Authors

Cited Authors

  • Islam, M; Sanghani, P; See, AAQ; James, ML; King, NKK; Ren, H

Published Date

  • January 1, 2019

Published In

Volume / Issue

  • 11383 LNCS /

Start / End Page

  • 456 - 463

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

International Standard Book Number 13 (ISBN-13)

  • 9783030117221

Digital Object Identifier (DOI)

  • 10.1007/978-3-030-11723-8_46

Citation Source

  • Scopus