Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects

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 contradict with the prior knowledge about normal tissue. Specifically, with the reference scans, we compute anomaly score maps for the scan with lesions, and these maps are used as auxiliary inputs to the segmentation network to aid brain lesion segmentation. The proposed strategy was evaluated on different brain lesion segmentation tasks, and the results indicate the benefit of incorporating the anatomical priors using our approach.