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Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks
H van Voorst1,2, P R Konduri3,2, L M van Poppel3,2
1From the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.) h.vanvoorst@amsterdamumc.nl.
AJNR. American Journal of Neuroradiology
|July 28, 2022
Summary
This study developed an unsupervised deep learning model for stroke lesion segmentation. The generative adversarial network achieved moderate accuracy for infarct lesions, showing promise for automated analysis.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Neurology
Background:
- Supervised deep learning is standard for stroke lesion segmentation on non-contrast computed tomography (NCCT).
- Supervised methods necessitate manual annotations, which are time-consuming and labor-intensive.
- Unsupervised deep learning methods, like generative adversarial networks (GANs), offer an alternative by not requiring manual annotations.
Purpose of the Study:
- To develop and evaluate a GAN for segmenting infarct and hemorrhagic stroke lesions on follow-up NCCT scans.
- To assess the efficacy of unsupervised deep learning in stroke lesion segmentation.
- To provide an automated tool for lesion segmentation, reducing reliance on manual annotation.
Main Methods:
- A GAN was trained on 820 patient NCCT scans from acute ischemic stroke trials.
- The GAN transformed follow-up scans to resemble baseline scans by generating a difference map.
- Lesion segmentations were extracted from the difference map and evaluated using Dice similarity coefficient, Bland-Altman analysis, and intraclass correlation coefficient.
Main Results:
- The GAN achieved a median Dice similarity coefficient of 0.31 for 24-hour and 0.59 for 1-week infarct lesions.
- Segmentation performance for hemorrhagic lesions was significantly lower (median Dice: 0.02-0.08).
- Good volumetric correspondence was observed for infarct lesions (ICC 0.83-0.90).
Conclusions:
- Unsupervised GANs can automate infarct lesion segmentation on NCCT with moderate accuracy.
- The developed GAN shows potential for clinical application in stroke imaging analysis.
- Further research may improve segmentation of hemorrhagic lesions and refine unsupervised deep learning models.
