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.
|Title of host publication||Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016|
|Subtitle of host publication||19th International Conference, Athens, Greece, October 17-21, 2016 : Proceedings|
|Editors||S. Ourselin, L. Joskowicz, M.R. Sabuncu, G. Unal, W. Wells|
|Publication status||Published - 2016|
|Name||Lecture Notes in Computer Science|