Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation

Zhewei Wang; Charles D. Smith; Jundong Liu

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arXiv · 1807.09298

Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation

Zhewei Wang·Charles D. Smith·Jundong Liu

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Abstract

In this paper, we develop a two-stage neural network solution for the challenging task of white-matter lesion segmentation. To cope with the vast vari- ability in lesion sizes, we sample brain MR scans with patches at three differ- ent dimensions and feed them into separate fully convolutional neural networks (FCNs). In the second stage, we process large and small lesion separately, and use ensemble-nets to combine the segmentation results generated from the FCNs. A novel activation function is adopted in the ensemble-nets to improve the segmen- tation accuracy measured by Dice Similarity Coefficient. Experiments on MICCAI 2017 White Matter Hyperintensities (WMH) Segmentation Challenge data demonstrate that our two-stage-multi-sized FCN approach, as well as the new activation function, are effective in capturing white-matter lesions in MR images.

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Zhewei Wang, Charles D. Smith, Jundong Liu. 2018-07-24. Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation. https://arxiv.org/abs/1807.09298

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