Prior-knowledge-informed deep learning for lacune detection and
  quantification using multi-site brain MRI
Bo Li Jeroen de Bresser Wiro Niessen Matthias van Osch Wiesje M. van der Flier Geert Jan Biessels Meike W. Vernooij Esther Bron
Lacunes of presumed vascular origin, also referred to as lacunar infarcts,
are important to assess cerebral small vessel disease and cognitive diseases
such as dementia. However, visual rating of lacunes from imaging data is
challenging, time-consuming, and rater-dependent, owing to their small size,
sparsity, and mimics. Whereas recent developments in automatic algorithms have
shown to make the detection of lacunes faster while preserving sensitivity,
they also showed a large number of false positives, which makes them
impractical for use in clinical practice or large-scale studies. Here, we
develop a novel framework that, in addition to lacune detection, outputs a
categorical burden score. This score could provide a more practical estimate of
lacune presence that simplifies and effectively accelerates the imaging
assessment of lacunes. We hypothesize that the combination of detection and the
categorical score makes the procedure less sensitive to noisy labels.