DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Cerebrovascular Diseases

Background:

• Cerebral Small Vessel Diseases (CSVD) affect brain health and are linked to neurodegeneration.
• Advanced 7 Tesla MRI offers higher resolution for visualizing small brain vessels.
• Traditional vessel segmentation methods struggle with small vessels, requiring manual effort.

Purpose of the Study:

• To develop a deep learning architecture for automatic small vessel segmentation in 7 Tesla 3D Time-of-Flight (ToF) MRA data.
• To improve the accuracy and efficiency of small vessel segmentation compared to existing methods.

Main Methods:

• A U-Net Multi-Scale Supervision deep learning model was employed.
• The model was trained on a small dataset of semi-automatically segmented MRA images.
• Deformation-aware learning was used for self-supervised equivariant training to enhance generalization.

Main Results:

• The proposed deep learning method achieved a Dice score of 80.44 ± 0.83 on the test set.
• Demonstrated an 18.98% improvement over manual segmentation in a specific region.
• Significantly outperformed traditional non-deep learning segmentation techniques.

Conclusions:

• The developed deep learning approach effectively segments small brain vessels in 7 Tesla MRA.
• This technique offers a more accurate and efficient alternative for studying CSVD and related neurological conditions.
• The deformation-aware learning strategy enhances the model's generalizability and performance.