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MedSAM/MedSAM2 Feature Fusion: Enhancing nnUNet for 2D TOF-MRA Brain Vessel Segmentation.

Han Zhong^1,2, Jiatian Zhang^1,2, Lingxiao Zhao^1,2

¹School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.

Journal of Imaging

|June 25, 2025

Summary

This summary is machine-generated.

This study introduces an AI method for segmenting brain arteries in TOF-MRA scans, improving accuracy for small vessels. The enhanced nnUNet model shows better segmentation and boundary localization, aiding stroke diagnosis.

Keywords:

MedSAM MedSAM2 TOF-MRA brain vessel segmentation nnUNet

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Accurate brain vessel segmentation is crucial for diagnosing cerebral stroke.
Current AI methods face challenges in segmenting small vessels and handling class imbalance.

Purpose of the Study:

To develop a novel 2D segmentation method for arterial vessels in TOF-MRA brain slices.
To enhance feature representation and address class imbalance for improved segmentation accuracy.

Main Methods:

Utilized the nnUNet framework as a baseline segmentation network.
Integrated MedSAM/MedSAM2 features to improve feature representation.
Incorporated focal loss to mitigate class imbalance issues.

Main Results:

The MedSAM2-enhanced model showed a 0.72% relative improvement in Dice coefficient compared to baseline nnUNet.
Reduced HD95 (mm) from 48.20 to 46.30 and ASD (mm) from 5.33 to 4.97.
Demonstrated significant enhancements in boundary localization and segmentation accuracy on the CAS2023 dataset.

Conclusions:

The proposed method effectively addresses the challenge of small vessel segmentation in TOF-MRA.
This approach has the potential to improve the clinical diagnosis of cerebrovascular diseases.