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

arXiv: https://arxiv.org/abs/1807.09298 · HTML: https://mlsanity.com/paper/1807.09298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models](https://mlsanity.com/paper/2210.06362/) (2022)

## Abstract (arXiv)

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.