ICHNet: Intracerebral hemorrhage (ICH) segmentation using deep learning
Conference Paper
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