Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.

ABSTRACT: White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. In the loss functions the network uses anatomical information regarding WMH spatial distribution in loss functions, to improve the efficiency of segmen

SUBMITTER: Sundaresan V 

PROVIDER: S-EPMC8505759 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature