Automated CT segmentation and analysis for acute middle cerebral artery stroke

The quantitative nature of CT should make it amenable to semiautomated analysis using modern neuroimaging methods. The purpose of this study was to begin to develop automated methods of analysis of CT scans to identify putative hypodensity within the lentiform nucleus and insula in patients with acute middle cerebral artery stroke.

Thirty-five CT scans were retrospectively selected from our CT archive (scans of 20 normal control participants and 15 patients presenting with acute middle cerebral artery stroke symptoms). The DICOM data for each participant were interpolated to a single volume, scalp stripped, normalized to a standard atlas, and segmented into anatomic regions. Voxel densities in the lentiform nucleus and insula were compared with the contralateral side at P <.01 using the Wilcoxon two-sample rank sum statistic, corrected for spatial autocorrelation.

The quality of the registration for the anatomic regions was excellent. The control group had two false-positive results. The patient group had two false-negative results in the lentiform nucleus, two false-negative results in the insular cortex, and one false-positive finding for the insular cortex. The remainder of the infarcts were correctly identified. The original clinical reading, performed at the time of presentation, produced five false-negative interpretations for the patient group, all of which were correctly identified by the automated algorithm.

We present an automated method for identifying potential areas of acute ischemia on CT scans. This approach can be extended to other brain regions and vascular territories and may aid in the interpretation of CT scans in cases of hyperacute stroke.