# Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation

arXiv: https://arxiv.org/abs/1807.05153 · HTML: https://mlsanity.com/paper/1807.05153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future](https://mlsanity.com/paper/2103.14969/) (2021)
- [A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs](https://mlsanity.com/paper/2302.09516/) (2023)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)

## Abstract (arXiv)

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.