We propose a multi-output segmentation approach, which incorporates other non-lesion brain tissue maps into the additional output layers to force the model to learn more about the lesion and tissue characteristics. We construct a cross-vendor study by training the white matter hyperintensities segmentation model on cases collected from one vendor and testing the model performance on eight different data sets. The model performance can be significantly improved, especially in testing sets which shows low image contrast similarity with training data, suggesting the feasibility of incorporating the non-lesion characteristics into segmentation model to enhance the resistance of cross-vendor image contrast variabilities.
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