Share Email Print
cover

Proceedings Paper

Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of natural scenes, with particular improvements in small object segmentation due to improved representative feature learning. Accordingly, we propose a novel, efficient 3DCNN based deep learning framework with context encoding for semantic brain tumor segmentation using multimodal magnetic resonance imaging (mMRI). The context encoding module in the proposed model enforces rich, class-dependent feature learning to improve the overall multi-label segmentation performance. We subsequently utilize context augmented features in a machine-learning based survival prediction pipeline to improve the prediction performance. The proposed method is evaluated using the publicly available 2019 Brain Tumor Segmentation (BraTS) and survival prediction challenge dataset. The results show that the proposed method significantly improves the tumor tissue segmentation performance and the overall survival prediction performance.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140H (16 March 2020); doi: 10.1117/12.2550693
Show Author Affiliations
Linmin Pei, Old Dominion Univ. (United States)
Lasitha Vidyaratne, Old Dominion Univ. (United States)
Md Monibor Rahman, Old Dominion Univ. (United States)
Khan M. Iftekharuddin, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray