# Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images

arXiv: https://arxiv.org/abs/1802.05203 · HTML: https://mlsanity.com/paper/1802.05203/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 16 of 16)

- [A Unified Framework for U-Net Design and Analysis](https://mlsanity.com/paper/2305.19638/) (2023)
- [Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2103.08454/) (2021)
- [Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation](https://mlsanity.com/paper/2210.17408/) (2022)
- [Attentive Symmetric Autoencoder for Brain MRI Segmentation](https://mlsanity.com/paper/2209.08887/) (2022)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging](https://mlsanity.com/paper/2106.00919/) (2021)
- [Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels](https://mlsanity.com/paper/2112.06693/) (2021)
- [Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition](https://mlsanity.com/paper/2307.10036/) (2023)
- [Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging](https://mlsanity.com/paper/2308.04395/) (2023)
- [Effect of Intensity Standardization on Deep Learning for WML Segmentation in Multi-Centre FLAIR MRI](https://mlsanity.com/paper/2307.03827/) (2023)
- [Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows](https://mlsanity.com/paper/2112.03277/) (2021)
- [P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI](https://mlsanity.com/paper/2403.13996/) (2024)
- [Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database](https://mlsanity.com/paper/2207.08467/) (2022)
- [Ensemble Methods for Multi-Organ Segmentation in CT Series](https://mlsanity.com/paper/2303.17956/) (2023)
- [3D SA-UNet: 3D Spatial Attention UNet with 3D Atrous Spatial Pyramid Pooling for White Matter Hyperintensities Segmentation](https://mlsanity.com/paper/2309.08402/) (2023)
- [SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions](https://mlsanity.com/paper/2510.26961/) (2025)

## Abstract (arXiv)

White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. In this paper, we provide detailed descriptions and quantitative analysis on key components of the system. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities and data augmentation affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to test the effect of ensemble size. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.