A 3-dimensional (3D) convolutional neural network is presented for the segmentation and quantification of spontaneous
intracerebral haemorrhage (ICH) in non-contrast computed tomography (NCCT). The method utilises a combination of
contextual information on multiple scales for fast and fully automatic dense predictions. To handle a large class imbalance
present in the data, a weight map is introduced during training. The method was evaluated on two datasets of 25 and 50
patients respectively. The reference standard consisted of manual annotations for each ICH in the dataset. Quantitative
analysis showed a median Dice similarity coefficient of 0.91 [0.87 - 0.94] and 0.90 [0.85 - 0.92] for the two test datasets in
comparison to the reference standards. Evaluation of a separate dataset of 5 patients for the assessment of the observer
variability produced a mean Dice similarity coefficient of 0.95 +/- 0.02 for the inter-observer variability and 0.97 +/- 0.01 for the
intra-observer variability. The average prediction time for an entire volume was 104 +/- 15 seconds. The results demonstrate
that the method is accurate and approaches the performance of expert manual annotation.