Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance

• DOI: 10.1016/j.jstrokecerebrovasdis.2021.105886
• Publication Date: 2021-06-25T01:13:58Z
• Authors: Min Jae Myung; Kyung Mi Lee; Hyug-Gi Kim; Janghoon Oh; Ji Young Lee; Ilah Shin; Eui Jong Kim; Jin San Lee
• Keywords: Adult; Aged, 80 and over; Male; Reproducibility of Results; 02 engineering and technology; Middle Aged; Magnetic Resonance Imaging; 3. Good health; Deep Learning; Predictive Value of Tests; Image Interpretation, Computer-Assisted; 0202 electrical engineering, electronic engineering, information engineering; Humans; Female; Aged; Cerebral Hemorrhage; Retrospective Studies

Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented.The proposed framework consists of the following phases: (1) pre-processing (skull extraction), (2) the first training with the ground truth labeled using CMB, (3) the second training with the ground truth labeled with CMB mimicking the same subjects, and (4) post-processing (cerebrospinal fluid (CSF) filtering). The proposed technique was validated on a dataset of 1133 CBMs that consisted of 5284 images for training and 1737 images for testing. We applied a two-stage approach using a region-based CNN method based on You Only Look Once (YOLO) to investigate a novel CMB detection technique.The sensitivity, precision, F1-score and false positive per person (FPavg) were evaluated as 80.96, 60.98, 69.57 and 6.57, 59.69, 62.70, 61.16 and 4.5, 66.90, 79.75, 72.76 and 2.15 for YOLO with a single label, YOLO with double labels, and YOLO + CSF filtering, respectively, and YOLO + CSF filtering showed the highest precision performance, F1-score and lowest FPavg.Using proposed framework, we developed an optimized CMB learning model with low false positives and a balanced performance in clinical practice.