Intracerebral haemorrhage segmentation in non-contrast CT

A. Patel, F. Schreuder, C. Klijn, M. Prokop, B. van Ginneken, H. Marquering, Y. Roos, M. Baharoglu, F. Meijer and R. Manniesing

Nature Scientific Reports 2019;9(1):17858.

DOI PMID Cited by ~1

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.

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