HIST: HyperIntensity Segmentation Tool
Accurate quantification of white matter hyperintensities (WMH) from MRI is a valuable tool for studies on ageing and neurodegeneration. Reliable automatic extraction of WMH biomarkers is challenging, primarily due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic and accurate method to segment these lesions that is based on the use of neural networks and an overcomplete strategy. The proposed method was compared to other related methods showing competitive and reliable results in two different neurodegenerative datasets.
KeywordsWhite Matter Hyperintensities Flair Image Neural Network Classifier Brain Mask Lesion Segmentation
This research has been done thanks to the Australian distinguished visiting professor grant and the Spanish “Programa de apoyo a la investigación y desarrollo (PAID-00-15)” of the Universidad Politécnica de Valencia. This study has been carried out with support from the French State, managed by the French National Research Agency in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.
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