# Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database

arXiv: https://arxiv.org/abs/2207.08467 · HTML: https://mlsanity.com/paper/2207.08467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)
- [Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images](https://mlsanity.com/paper/1802.05203/) (2018)

## Abstract (arXiv)

Automated segmentation of white matter hyperintensities (WMHs) is an essential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer's disease (AD). Clinical MRI protocols migrate to a three-dimensional (3D) FLAIR-weighted acquisition to enable high spatial resolution in all three voxel dimensions. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Among 441 participants (194 male, mean age: (64.91 +/- 9.32) years) from the DDI study, two in-house networks were trained and validated across five national collection sites. Three models were tested on a held-out subset of the internal data from the 441 participants and an external dataset with 29 cases from an international collaborator. These test sets were evaluated independently. Five established WMH performance metrics were used for comparison against ground truth human-in-the-loop segmentation. Results of the three networks tested, the 3D nnU-Net had the best performance with an average dice similarity coefficient score of 0.76 +/- 0.16, performing better than both the in-house developed 2.5D model and the SOTA Deep Bayesian network. With the increasing use of 3D FLAIR-weighted images in MRI protocols, our results suggest that WMH segmentation models can be trained on 3D data and yield WMH segmentation performance that is comparable to or better than state-of-the-art without the need for including T1-weighted image series.