Renata G. Raidou, Hugo J. Kuijf, Neda Sepasian, Nicola Pezzotti,
Wilhelm H. Bouwy, Marcel Breeuwer, and Anna Vilanova.
(In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016. Lecture Notes in Computer Science, vol. 9901, pp.97–105. Springer, Cham.; doi)

Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end, classifiers are often trained – usually, using T1 and FLAIR weighted MR images. Incorporating additional features, derived from diffusion weighted MRI, could improve classification. However, the multitude of diffusion-derived features requires selecting the most adequate. For this, automated feature selection is commonly employed, which can often be sub-optimal. In this work, we propose a different approach, introducing a semi-automated pipeline to select interactively features for WMH classification. The advantage of this solution is the integration of the knowledge and skills of experts in the process. In our pipeline, a Visual Analytics (VA) system is employed, to enable user-driven feature selection. The resulting features are T1, FLAIR, Mean Diffusivity (MD), and Radial Diffusivity (RD) – and secondarily, CS, and Fractional Anisotropy (FA). The next step in the pipeline is to train a classifier with these features, and compare its results to a similar classifier, used in previous work with automated feature selection. Finally, VA is employed again, to analyze and understand the classifier performance and results.

BibTeX

@inproceedings{raidou2016employing,
  title={Employing visual analytics to aid the design of white matter hyperintensity classifiers},
  author={Raidou, Renata Georgia and Kuijf, Hugo J and Sepasian, Neda and Pezzotti, Nicola and Bouvy, Willem H and Breeuwer, Marcel and Vilanova, Anna},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={97--105},
  year={2016},
  organization={Springer}
}
Scroll to Top