  Lacunes of presumed vascular origin (lacunes) are associated with an
increased risk of stroke, gait impairment, and dementia and are a primary
imaging feature of the small vessel disease. Quantification of lacunes may be
of great importance to elucidate the mechanisms behind neuro-degenerative
disorders and is recommended as part of study standards for small vessel
disease research. However, due to the different appearance of lacunes in
various brain regions and the existence of other similar-looking structures,
such as perivascular spaces, manual annotation is a difficult, elaborative and
subjective task, which can potentially be greatly improved by reliable and
consistent computer-aided detection (CAD) routines.
  In this paper, we propose an automated two-stage method using deep
convolutional neural networks (CNN). We show that this method has good
performance and can considerably benefit readers. We first use a fully
convolutional neural network to detect initial candidates. In the second step,
we employ a 3D CNN as a false positive reduction tool. As the location
information is important to the analysis of candidate structures, we further
equip the network with contextual information using multi-scale analysis and
integration of explicit location features. We trained, validated and tested our
networks on a large dataset of 1075 cases obtained from two different studies.
Subsequently, we conducted an observer study with four trained observers and
compared our method with them using a free-response operating characteristic
analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits
performance similar to the trained human observers and achieves a sensitivity
of 0.974 with 0.13 false positives per slice. A feasibility study also showed
that a trained human observer would considerably benefit once aided by the CAD
system.
