Segmentation of both large and small white matter hyperintensities/lesions in
brain MR images is a challenging task which has drawn much attention in recent
years. We propose a multi-scale aggregation model framework to deal with
volume-varied lesions. Firstly, we present a specifically-designed network for
small lesion segmentation called Stack-Net, in which multiple convolutional
layers are connected, aiming to preserve rich local spatial information of
small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale
Stack-Nets with different receptive fields to learn multi-scale contextual
information of both large and small lesions. Our model is evaluated on recent
MICCAI WMH Challenge Dataset and outperforms the state-of-the-art on lesion
recall and lesion F1-score under 5-fold cross validation. In addition, we
further test our pre-trained models on a Multiple Sclerosis lesion dataset with
30 subjects under cross-center evaluation. Results show that the aggregation
model is effective in learning multi-scale spatial information.It claimed the
first place on the hidden test set after independent evaluation by the
challenge organizer. In addition, we further test our pre-trained models on a
Multiple Sclerosis lesion dataset with 30 subjects under cross-center
evaluation. Results show that the aggregation model is effective in learning
multi-scale spatial information.

使用多尺度聚合模型来处理大小可变的白质高信号区域 / 损伤的分割任务，在多个数据集上的表现均优于现有的方法。