Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury

• Published in: NeuroImage clinical Vol. 12; no. C; pp. 241 - 251
• Main Authors: van den Heuvel, T L A, van der Eerden, A W, Manniesing, R, Ghafoorian, M, Tan, T, Andriessen, T M J C, Vande Vyvere, T, van den Hauwe, L, Ter Haar Romeny, B M, Goraj, B M, Platel, B
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier 01-01-2016
• Subjects: Adult; Brain Hemorrhage, Traumatic - complications; Brain Hemorrhage, Traumatic - diagnostic imaging; Cerebral Hemorrhage - complications; Cerebral Hemorrhage - diagnostic imaging; Cerebral Hemorrhage - pathology; Cerebral Microbleeds; Computer Aided Detection; Female; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging; Male; Pattern Recognition, Automated - methods; Regular; Susceptibility Weighted Imaging; Traumatic Brain Injury; Susceptibility Weighted Imaging; Traumatic Brain Injury; Cerebral Microbleeds; Computer Aided Detection
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2016.07.002

In this paper a Computer Aided Detection (CAD) system is presented to automatically detect Cerebral Microbleeds (CMBs) in patients with Traumatic Brain Injury (TBI). It is believed that the presence of CMBs has clinical prognostic value in TBI patients. To study the contribution of CMBs in patient outcome, accurate detection of CMBs is required. Manual detection of CMBs in TBI patients is a time consuming task that is prone to errors, because CMBs are easily overlooked and are difficult to distinguish from blood vessels. This study included 33 TBI patients. Because of the laborious nature of manually annotating CMBs, only one trained expert manually annotated the CMBs in all 33 patients. A subset of ten TBI patients was annotated by six experts. Our CAD system makes use of both Susceptibility Weighted Imaging (SWI) and T1 weighted magnetic resonance images to detect CMBs. After pre-processing these images, a two-step approach was used for automated detection of CMBs. In the first step, each voxel was characterized by twelve features based on the dark and spherical nature of CMBs and a random forest classifier was used to identify CMB candidate locations. In the second step, segmentations were made from each identified candidate location. Subsequently an object-based classifier was used to remove false positive detections of the voxel classifier, by considering seven object-based features that discriminate between spherical objects (CMBs) and elongated objects (blood vessels). A guided user interface was designed for fast evaluation of the CAD system result. During this process, an expert checked each CMB detected by the CAD system. A Fleiss' kappa value of only 0.24 showed that the inter-observer variability for the TBI patients in this study was very large. An expert using the guided user interface reached an average sensitivity of 93%, which was significantly higher (p = 0.03) than the average sensitivity of 77% (sd 12.4%) that the six experts manually detected. Furthermore, with the use of this CAD system the reading time was substantially reduced from one hour to 13 minutes per patient, because the CAD system only detects on average 25.9 false positives per TBI patient, resulting in 0.29 false positives per definite CMB finding.