  Lacunes of presumed vascular origin are fluid-filled cavities of between 3 -
15 mm in diameter, visible on T1 and FLAIR brain MRI. Quantification of lacunes
relies on manual annotation or semi-automatic / interactive approaches; and
almost no automatic methods exist for this task. In this work, we present a
two-stage approach to segment lacunes of presumed vascular origin: (1)
detection with Mask R-CNN followed by (2) segmentation with a U-Net CNN. Data
originates from Task 3 of the "Where is VALDO?" challenge and consists of 40
training subjects. We report the mean DICE on the training set of 0.83 and on
the validation set of 0.84. Source code is available at:
https://github.com/hjkuijf/MixLacune . The docker container hjkuijf/mixlacune
can be pulled from https://hub.docker.com/r/hjkuijf/mixlacune .
