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Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Brain Tumor Segmentation Using Deep Fully Convolutional Neural Networks

Author : Geena Kim

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

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Abstract

In this study, brain tumor substructures are segmented using 2D fully convolutional neural networks. A number of modifications such as double convolution layers, inception modules, and dense modules were added to a U-Net to achieve a deep architecture and test if the increased depth improves the performance. The experiments show that the deep architectures improve the performance. Also, the performance is enhanced from ensembling across the models trained on images in different orientations and ensembling across the models with different architectures. Even without any data augmentation, the ensembled model achieves a competitive performance and generalizes well on a new dataset. The resulting mean 3D Dice scores (ET/WT/TC) on the BRATS17 validation and test sets are 0.75/0.88/0.73 and 0.72/0.86/0.73.

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previous chapter Brain Tumor Segmentation and Parsing on MRIs Using Multiresolution Neural Networks
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Metadata
Title
Brain Tumor Segmentation Using Deep Fully Convolutional Neural Networks
Author
Geena Kim
Copyright Year
2018
Book
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-319-75237-2

Electronic ISBN: 978-3-319-75238-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75238-9_30

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