Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

•We developed a method for automatic brain tissue classification on CT images.•Used appropriately, the method may obviate the clinical need for MRI to quantify brain atrophy.•A U-Net-based deep learning model was trained on CT images with labels generated by segmenting paired MR images.•High overlap scores indicate strong agreement of the model predictions with segmentations.•High volumetric correlations with low volumetric errors were observed for grey matter, white matter, and intracranial volume, indicating that model-derived tissue volumes were similar to MRI-derived volumes.•We provide extensive benchmarking of the label agreement result against MRI-based segmentation methods. Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research. [Display omitted]