Related Experiment Video
Updated: Aug 8, 2026
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Automatic coarse-to-fine AC-PC localization on CT using registration-guided 3D-UNets
Sharada Kadaba Sridhar1,2, Alyssa Eastman2, Peter Wilson3
1Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States.
Introduction:
Automatically localizing the Anterior Commissure (AC) and Posterior Commissure (PC) is foundational for CT-based algorithmic disease screening, yet robust computational methods for this on CT remain lacking. We developed a registration-guided 3D-UNet framework for CT-based AC-PC localization, demonstrating its utility in computing ventriculomegaly features for Normal Pressure Hydrocephalus (NPH) detection.
Methods:
Framework development and evaluation were on an internal cohort of scans from patients with NPH, Alzheimer's disease, post-traumatic volume loss, and headache (Veterans Affairs [VA]-Cohort, n = 427). External validation was on separate datasets (VA-ExtCohort, University of California, Santa Barbara [UCSB]-ExtCohort). AC-PC reference standard definition, model development, and evaluation were on 1 mm3-resampled scans.
Results:
On 1-mm3 resampled scans, test-set AC-PC mean radial errors (MREs) were 1.64/1.49 mm on the VA-Cohort, 2.42/1.79 mm on the VA-ExtCohort (n = 40), and 2.31/1.93 mm on the UCSB-ExtCohort (n = 43). Notably, the upper limits of the 95% confidence intervals (CIs) for localization errors across all cohorts were well below 3.2 mm; we empirically determined this to be a clinically relevant threshold beyond which the discriminative power of AC-PC-referenced ventriculomegaly features degrades. Ventriculomegaly features assessed using our framework's predictions successfully distinguished NPH from Alzheimer's disease, post-traumatic volume loss, and headache on a chart-verified VA-Cohort subset (n = 238) with a test-set Area Under the Receiver Operating Characteristic Curve (AUC) of 0.95, closely matching the performance of features assessed using manual AC-PC localization.
Conclusion:
The proposed registration-guided 3D-UNet framework accurately and automatically localizes the AC-PC on CT despite varied structural degeneration, enabling standardized radiological feature computation. This approach can augment neurodegenerative disease screening on CT, the primary modality for elderly patients evaluated for falls and altered mentation.
