Pneumocephalus, accumulation of air in intracranial space, can lead to midline shift and compression of brain. In this work, we detail the development of deep learning algorithms for automated detection and localization of pneumocephalus in head CT scans.
Firstly, to localize the intracranial space from a given head CT scan, a skullÂ-stripping algorithm was developed using a randomly sampled anonymized dataset of 78 head CT scans (1608 slices). We sampled another anonymized dataset containing 83 head CT scans (3546 slices) having pneumocephalus and 310 normal head CT scans which were randomly sampled to represent natural distribution. These 3546 slices (932 slices had pneumocephalus) were annotated for pneumocephalus regions. Then UÂNet based deep neural network algorithm was trained on these scans to accurately predict the pneumocephalus region . The predicted pneumocephalus region is refined by removing the regions outside the intracranial space identified by the skull stripping algorithm. The refined pneumocephalus region is then used to extract features. Using these features, a random forest was trained to classify the presence of pneumocephalus in a scan. Areas under receiver operating characteristics curves (AUC) were used to evaluate the algorithms.
An independent dataset of 1891 head CT scans (40 scans had pneumocephalus) was used for testing above algorithms. AUC for the scan level predictions was 0.89. Sensitivity and Specificity of 0.80 and 0.83 respectively were observed.
In this work, we showed the efficacy of deep learning algorithms in localizing and classifying the pneumocephalus accurately in a head CT scan.