Segmentation of incident lacunes during the course of ischemic cerebral small vessel diseases - Lebenberg, Jessica; Zhang, Ruiting; Grosset, Lina; Guichard, Jean Pierre; Fernandes, Fanny; Jouvent, Eric; Chabriat, Hugues (Frontiers in Neurology, 2023)
Background: Lacunes represent key imaging markers of cerebral Small Vessel Diseases (cSVD). During their progression, incident lacunes are related to stroke manifestations and contribute to progressive cognitive and/or motor decline. Assessing new lesions has become crucial but remains time-consuming and error-prone even for an expert. We thus sought to develop and validate an automatic segmentation method of incident lacunes in CADASIL, a severe and progressive monogenic form of cSVD. Methods: Incident lacunes were identified based on difference maps of 3D-T1-weighted-MRIs obtained at baseline and 2 years later. These maps were thresholded using clustering analysis and compared to results obtained by expert visual analysis considered as the gold standard approach. Results: The median number of lacunes at baseline in 30 randomly selected patients was 7 (IQR = [2;11]). The median number of incident lacunes was 2 (IQR = [0;3]) using the automatic method (mean time-processing: 25s/patient), 0.5 (IQR = [0;2]) using the standard visual approach (mean time-processing: 8min/patient). Complementary analysis of segmentation results enabled to remove quickly false positives detected in specific locations and to identify true incident lesions not previously detected by the standard analysis (2min/case). A combined approach based on automatic segmentation of incident lacunes followed by quick corrections of false positives allowed to reach high individual sensitivity (median at 0.66, IQR = [0.21; 1.00]) and global specificity scores (0.80). Conclusion: Automatic segmentation of incident lacunes followed by quick corrections of false positives appears promising for quantifying properly and rapidly incident lacunes in large cohorts of cSVD.