title: Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.
abstract: 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.
authors.affiliation: Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands; Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Ghafoorian
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
authors.affiliation: Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Mayra
authors.initials: M
authors.lastname: Bergkamp
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Joost
authors.initials: J
authors.lastname: Wissink
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Jiri
authors.initials: J
authors.lastname: Obels
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Karlijn
authors.initials: K
authors.lastname: Keizer
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Frank-Erik
authors.initials: FE
authors.lastname: de Leeuw
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Bram van
authors.initials: BV
authors.lastname: Ginneken
authors.affiliation: Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
authors.forename: Elena
authors.initials: E
authors.lastname: Marchiori
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: Platel
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000000981:diagnostic imaging*; D015331:Cohort Studies; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D059409:Stroke, Lacunar / Q000000981:diagnostic imaging*
keywords: Automated detection; Convolutional neural networks; Deep learning; Lacunes; Location-aware; Multi-scale