Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images.

ABSTRACT: Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We used datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For the source domain, we considered a dataset consisting of data acquired f

SUBMITTER: Sundaresan V 

PROVIDER: S-EPMC8573594 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature