A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning

• Published in: NeuroImage clinical 2018-01, Vol.20, p.498-505
• Main Authors: Morrison, Melanie A., Payabvash, Seyedmehdi, Chen, Yicheng, Avadiappan, Sivakami, Shah, Mihir, Zou, Xiaowei, Hess, Christopher P., Lupo, Janine M.
• Format: Article
• Language: English
• Subjects: Algorithm; Algorithms; Automated; Brain tumor; Cerebral Hemorrhage - diagnostic imaging; Cerebral microbleeds; Cohort Studies; Data Analysis; Humans; Image Processing, Computer-Assisted - methods; Lesion; Machine Learning; Magnetic resonance imaging; Microcirculation - physiology; Radiation therapy; Regular; Retrospective Studies; Susceptibility weighted imaging; Vascular injury; Vascular System Injuries - diagnostic imaging
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2018.08.002

With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data. Existing computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images. The initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99]. This development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler). •We modified our existing semi-automated microbleed detection method•Using our new method, specificity is increased and detection time is decreased•The inter-rater variability for detecting microbleeds is reduced with our method•Automatically labelled microbleed data can be used in machine learning methods•Our method has successfully detected microbleeds in multiple clinical populations