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Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation

  • Peter D. ChangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

In this paper, a fully convolutional residual neural network (FCR-NN) based on linear identity mappings is implemented for medical image segmentation, employed here in the setting of brain tumors. Inspired by deep residual networks which won the ImageNet ILSVRC 2015 classification challenge, the FCR-NN combines optimization gains from residual identity mappings with a fully convolutional architecture for image segmentation that efficiently accounts for both low- and high-level image features. After training two separate networks, one for the task of whole tumor segmentation and a second for tissue sub-region segmentation, the serial FCR-NN architecture exceeds state-of-the art with complete tumor, core tumor and enhancing tumor validation Dice scores of 0.87, 0.81 and 0.72 respectively. Despite each FCR-NN comprising a complex 22 layer architecture, the fully convolutional design allows for complete segmentation of a tumor volume within 2 s.

Keywords

Convolutional Neural Network Residual Function Tumor Segmentation Forward Pass Recursive Topology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author of this paper gratefully acknowledges the support of NVIDIA Corporation with the donation of GeForce GTX Titan X (12 GB) GPU used for this research.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of RadiologyColumbia University Medical CenterNew York CityUSA

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