%0 Journal Article
%T Deep multi-scale location-aware 3D convolutional neural networks for
automated detection of lacunes of presumed vascular origin
%A Bram Platel
%A Bram£¿van Ginneken
%A Elena Marchiori
%A Frank-Erik de Leeuw
%A Jiri Obels
%A Joost Wissink
%A Karlijn Keizer
%A Mayra Bergkamp
%A Mohsen Ghafoorian
%A Nico Karssemeijer
%A Tom Heskes
%J Archive of "NeuroImage : Clinical".
%D 2017
%R 10.1016/j.nicl.2017.01.033
%X 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
%K Lacunes
%K Automated detection
%K Convolutional neural networks
%K Deep learning
%K Multi-scale
%K Location-aware
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322213/