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Convolutional neural networks (CNNs) have greatly improved the performance of brain lesion segmentation. However, accurate segmentation of brain lesions can still be challenging when the appearance of lesions is similar to normal brain tissue. To address this problem, in this work we seek to exploit the information in scans of healthy subjects to improve brain lesion segmentation, where anatomical priors about normal brain tissue can be taken into account for better discrimination of lesions. To incorporate such prior knowledge, we propose to register a set of reference scans of healthy subjects to each scan with lesions, and the registered reference scans provide reference intensity samples of normal tissue at each voxel. In this way, the spatially adaptive prior knowledge can indicate the existence of abnormal voxels even when their intensities are similar to normal tissue, because their locations …