To develop and validate a deep neural network-based algorithm for automated, rapid and accurate detection from head CT for the following haemorrhages: intracerebral (ICH), subdural (SDH), extradural (EDH) and subarachnoid (SAH).
An anonymised database of head CTs was searched for non-contrast scans which were reported with any of ICH, SDH, EDH, SAH and those which were reported with neither of these. Each slice of these scans is manually tagged with the haemorrhages that are visible in that slice. In all, 3040 scans (116227 slices) were annotated, of which number of scans(slices) with ICH, SDH, EDH, SAH and neither of these are 781(6957), 493(6593), 742(6880), 561(5609) and 944(92999), respectively. Our deep learning model is a modified ResNet18 with 4 parallel final fully connected layers for each of the haemorrhages. This model is trained on the slices from the annotated dataset to make slice-level decisions. Random forests are trained with ResNet’s softmax outputs for all the slices in a scan as features to make scan-level decisions.
A different set of 2993 scans, uniformly sampled from the database without any exclusion criterion, is used for testing the scan-level decisions. Number of scans with ICH, SDH, EDH and SAH in this set are 123, 58, 41 and 62, respectively. Area under the receiver operating curve (AUC) for scan-level decisions for ICH, SDH, EDH and SAH are 0.91, 0.90, 0.90 and 0.90, respectively. Algorithm takes less than 1s to produce the decision for a scan.
Deep learning can accurately detect intra- and extra-axial haemorrhages from head CTs.