Year: 2017
Type: preprint
Abstract: We explore encoding brain symmetry into a neural network for a brain tumor segmentation task. A healthy human brain is symmetric at a high level of abstraction, and the high-level asymmetric parts are more likely to be tumor regions. Paying more attention to asymmetries has the potential to boost the performance in brain tumor segmentation. We propose a method to encode brain symmetry into existing neural networks and apply the method to a state-of-the-art neural network for medical imaging segmentation. We evaluate our symmetry-encoded network on the dataset from a brain tumor segmentation challenge and verify that the new model extracts information in the training images more efficiently than the original model.
Source: arXiv (Cornell University)
Authors: Zhang, Hejia, Zhu, Xia, Willke, Theodore L.
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2769469779