Self-Supervised Vessel Enhancement Using Flow-Based Consistencies.

Area of Science:

• Medical Imaging
• Computer Vision
• Machine Learning

Background:

• Supervised vessel segmentation requires extensive expert annotation, which is time-consuming and often limited to 2D datasets.
• Existing unsupervised methods for vessel segmentation rely on handcrafted features, leading to complex, dataset-specific pipelines that lack generalizability.

Purpose of the Study:

• To develop a generalizable self-supervised learning method for vessel segmentation across different modalities.
• To overcome the limitations of data annotation in supervised methods and the sensitivity of unsupervised approaches.

Main Methods:

• A novel self-supervised method is proposed, utilizing tube-like structure properties (connectivity, profile consistency, bifurcation) as inductive bias.
• A vector field, termed 'flow', is generated to model these inherent properties of vessels.
• The method is designed with a limited number of hyperparameters for improved generalizability.

Main Results:

• Experiments on diverse 2D and 3D public datasets demonstrate superior performance compared to unsupervised methods.
• The method successfully learns transferable features from unlabeled data, outperforming generic self-supervised approaches.
• Learned features are shown to be vessel-relevant and beneficial for downstream supervised segmentation tasks, especially with limited annotated data.

Conclusions:

• The proposed self-supervised method offers a robust and generalizable alternative for vessel segmentation.
• It effectively leverages unlabeled data to learn meaningful, vessel-specific features, reducing reliance on extensive manual annotation.
• This approach holds significant potential for clinical applications where annotated data is scarce.