  Localization of focal vascular lesions on brain MRI is an important component
of research on the etiology of neurological disorders. However, manual
annotation of lesions can be challenging, time-consuming and subject to
observer bias. Automated detection methods often need voxel-wise annotations
for training. We propose a novel approach for automated lesion detection that
can be trained on scans only annotated with a dot per lesion instead of a full
segmentation. From the dot annotations and their corresponding intensity images
we compute various distance maps (DMs), indicating the distance to a lesion
based on spatial distance, intensity distance, or both. We train a fully
convolutional neural network (FCN) to predict these DMs for unseen intensity
images. The local optima in the predicted DMs are expected to correspond to
lesion locations. We show the potential of this approach to detect enlarged
perivascular spaces in white matter on a large brain MRI dataset with an
independent test set of 1000 scans. Our method matches the intra-rater
performance of the expert rater that was computed on an independent set. We
compare the different types of distance maps, showing that incorporating
intensity information in the distance maps used to train an FCN greatly
improves performance.
