Efficient automated quantification of midline shift in intracerebral hemorrhage using a binarized deep learning model...

Accurate assessment of midline shift (MLS) is critical in the management of patients with acute intracerebral hemorrhage. Manual estimation of MLS by radiologists is time-consuming and subject to inter-observer variability. We present a lightweight, reproducible deep learning model designed for real-time, automated MLS quantification using non-contrast head CT scans.

We propose a binarized convolutional neural network based on a residual U-Net architecture that dramatically reduces model parameters (from 31 million to 44 thousand) using XNOR-based binarization and channel-wise scaling. The model segments the cerebral hemispheres and extracts midline features to quantify MLS. Post-processing involves the derivation of the deformed midline (dML) using edge detection and an anatomically guided ideal midline (iML) for reference. MLS is calculated as the maximum horizontal displacement between these lines.

Evaluated on the RSNA 2019 hemorrhage CT dataset (held-out test: 5,000 slices), the model achieved Dice scores of 0.92 (left) and 0.91 (right) and a mean absolute error (MAE) of 0.09 mm for MLS. On an NVIDIA GTX 1650 (4 GB), median inference time was 5.6 ms per slice for the segmentation step; all physical measurements were computed at native DICOM spacing.

This clinically-driven, resource-efficient model enables accurate MLS quantification suitable for emergency neuroimaging settings. Its reproducibility and low computational footprint make it ideal for deployment in low-resource environments, providing radiologists with rapid, decision-support insights. This lightweight model may assist accurate MLS quantification in time-sensitive settings while operating within constrained compute budgets. Further multicenter validation is warranted.