Features of generalised cerebral atrophy on brain CT images are the marker of neurodegenerative diseases of the brain. Our study aims at automated diagnosis of generalised cerebral atrophy on brain CT images using deep neural networks thereby offering an objective early diagnosis.
An anonymised dataset containing 78 head CT scans (1608 slices) was used to train and validate a skull-stripping algorithm. The intracranial region was marked out slice by slice in each scan. Then a U-Net-based deep neural network was trained on these annotations to strip the skull from each slice. A second anonymised dataset containing 2189 CT scans (231 scans with atrophy) was used to train and validate an atrophy detection algorithm. First, an image registration technique was applied on the predicted intracranial region to align all scans to a standard head CT scan. The parenchymal and CSF volume was calculated by thresholding Hounsfield units from the intracranial region. The ratio of CSF volume to parenchymal volume from each slice of the aligned CT scan and the age of the patient were used as features to train a random forest algorithm that decides if the scan shows generalised cerebral atrophy.
An independent set of 3000 head CT scans (347 scans with atrophy) was used to test the algorithm. Area under the receiver operating curve (AUC) for scan-level decisions is 0.86. Predictions on each patient takes time < 45s.
Deep convolutional networks can accurately detect generalised cerebral atrophy given a CT scan.