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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Comparing deep learning stroke segmentation in NCCT, CTA, and CTP: Accuracy, domain transfer, and temporal sampling

Linda Vorberg^1,2, Hendrik Ditt², Andreas Maier¹

¹Pattern Recognition Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.

Medical Physics

|April 3, 2026

Summary

Advanced CT imaging, including CT perfusion (CTP), significantly improves deep learning-based stroke lesion segmentation accuracy compared to non-contrast CT (NCCT) and CT angiography (CTA). Fine-tuning models enhances generalizability across clinical sites.

Keywords:

computed tomography segmentation stroke

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Published on: January 18, 2018

Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Neurology and stroke research

Background:

Stroke diagnosis relies on multiple CT image types: NCCT, CTA, and CTP.
CTP and mCTA offer advanced hemodynamic insights crucial for treatment decisions.
Deep learning (DL) models can automate stroke lesion segmentation, but generalizability is key for clinical use.

Purpose of the Study:

To compare the effectiveness of NCCT, CTA, mCTA, and CTP for DL-based stroke lesion segmentation.
To guide modality selection based on imaging availability and assess model transferability.
To investigate the impact of temporal sampling from CTP scans on segmentation performance.

Main Methods:

Trained nnU-Net models on 91 stroke patients' NCCT, CTA, mCTA, and CTP data for lesion segmentation.
Assessed model transferability using a pre-trained model from another site, with and without fine-tuning.
Evaluated various temporal sampling strategies for 4D CTP data input.

Main Results:

Advanced imaging improved stroke core segmentation accuracy: Dice coefficient increased from 0.36 (NCCT) to 0.78 (CTP) for larger infarcts.
Fine-tuning consistently improved model generalizability across all image types.
Temporal sampling strategies for CTP showed variable results, with no clear trend across different time points or infarct sizes.

Conclusions:

Automated stroke lesion segmentation using nnU-Net varies in quality across CT acquisition types.
Multi-timepoint imaging (CTP, mCTA) significantly outperforms NCCT and CTA in segmentation performance.