Document Type

Conference Paper

Publication Date

2020

DOI

10.1117/12.2550693

Publication Title

Proceedings of SPIE

Volume

11314

Pages

11314OH (1-8)

Conference Name

Medical Imaging 2020: Computer-Aided Diagnosis, 16-19 February 2020, Houston, Texas, U.S.A.

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.

Comments

"SPIE grants to authors (and their employers) of papers, posters, and presentation recordings published in SPIE Proceedings or SPIE Journals on the SPIE Digital Library (hereinafter "publications") the right to post an author-prepared version or an official version (preferred version) of the publication on an internal or external server controlled exclusively by the author/employer or the entity funding the research, provided that (a) such posting is noncommercial in nature and the publication is made available to users without charge; (b) an appropriate copyright notice and citation appear with the publication; and (c) a link to SPIE's official online version of the publication is provided using the item's DOI."

© 2020 SPIE

Publisher's version available at: https://doi.org/10.1117/12.2550693

Original Publication Citation

Pei, L., Vidyaratne, L., Rahman, M. M., & Iftekharuddin, K. (2020) Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction. In Hahn, H.K. & Mazurowski, M.A. (Eds.) Medical Imaging 2020: Computer-Aided Diagnosis, 16-19 February 2020 Houston, Texas, U.S.A. 11314OH (pp. 1-8) SPIE. https://doi.org/10.1117/12.2550693.

ORCID

0000-0003-4053-7948 (Vidyaratne)

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