# Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities

arXiv: https://arxiv.org/abs/1610.04834 · HTML: https://mlsanity.com/paper/1610.04834/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2016.

## Built on by (most cited first, 13 of 13)

- [Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI](https://mlsanity.com/paper/2312.05119/) (2023)
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- [MixLacune: Segmentation of lacunes of presumed vascular origin](https://mlsanity.com/paper/2108.02483/) (2021)
- [P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI](https://mlsanity.com/paper/2403.13996/) (2024)
- [A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2412.06088/) (2024)
- [Spectral Machine Learning for Pancreatic Mass Imaging Classification](https://mlsanity.com/paper/2105.00728/) (2021)
- [Effect of Prior-based Losses on Segmentation Performance: A Benchmark](https://mlsanity.com/paper/2201.02428/) (2022)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [CNN-based Classification Framework for Lung Tissues with Auxiliary Information](https://mlsanity.com/paper/2206.06701/) (2022)
- [Automatic location detection based on deep learning](https://mlsanity.com/paper/2403.10912/) (2024)
- [Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning](https://mlsanity.com/paper/2506.22041/) (2025)
- [Signal Intensity-weighted coordinate channels improve learning stability and generalisation in 1D and 2D CNNs in localisation tasks on biomedical signals](https://mlsanity.com/paper/2511.03645/) (2025)

## Abstract (arXiv)

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with hand-crafted features as well as CNNs that do not integrate location information. On a test set of 46 scans, the best configuration of our networks obtained a Dice score of 0.791, compared to 0.797 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value=0.17).