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 .
@article{arxiv.2108.02483,
title = {MixLacune: Segmentation of lacunes of presumed vascular origin},
author = {Denis Kutnar and Bas H. M. van der Velden and Marta Girones Sanguesa and Mirjam I. Geerlings and J. Matthijs Biesbroek and Hugo J. Kuijf},
journal= {arXiv preprint arXiv:2108.02483},
year = {2021}
}
Comments
Submitted to the "Where is VALDO?" challenge, MICCAI 2021