MixLacune: Segmentation of lacunes of presumed vascular origin
Denis Kutnar, Bas H. M. van der Velden, Marta Girones Sanguesa, Mirjam I. Geerlings, J. Matthijs Biesbroek, Hugo J. Kuijf
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/hjkuijf/MixLacuneOfficialIn paperpytorch★ 7
Abstract
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 .