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Related Experiment Video

Updated: Jun 20, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Self-supervised learning for stroke lesion segmentation on CT: a new pretext task for neuroimaging.

Juliette Moreau, Laura Mechtouff, David Rousseau

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

Summary

This study introduces a new self-supervised learning method for medical imaging. The novel pretext task improves stroke lesion segmentation accuracy on CT scans, offering a more efficient approach for neuroimaging analysis.

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroimaging

Background:

Deep learning models require large annotated datasets for medical image analysis.
Annotation is costly, driving the need for methods like self-supervised learning (SSL).
SSL uses pretext tasks to pre-train models on unannotated data.

Purpose of the Study:

To propose a novel SSL pretext task tailored for neuroimaging.
To demonstrate its effectiveness in stroke lesion segmentation on CT scans.
To improve segmentation performance and stability compared to generic pretext tasks.

Main Methods:

Developed a novel pretext task leveraging the stroke ASPECTS score for neuroimaging.
Pre-trained a model using this task on an initial dataset.
Fine-tuned the model on target data for stroke lesion segmentation.

Main Results:

Achieved improved segmentation performance and stability for stroke lesions.
Demonstrated superior efficiency compared to generic pretext tasks in self-supervised learning.
Validated the effectiveness of the neuroimaging-specific pretext task.

Conclusions:

The proposed pretext task is efficient and effective for stroke lesion segmentation.
Tailoring pretext tasks to neuroimaging challenges enhances SSL performance.
This method supports accurate and reliable clinical workflows for stroke diagnosis.