MixLacune: Segmentation of lacunes of presumed vascular origin
This work addresses the need for automated quantification of lacunes in medical imaging, offering a tool for researchers and clinicians, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of automatically segmenting lacunes of presumed vascular origin in brain MRI, which previously relied on manual or semi-automatic methods, and reports a mean DICE score of 0.83 on the training set and 0.84 on the validation set using a two-stage approach.
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 .