Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
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Vessel segmenting is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious and mostly limited for two-dimensional datasets with a small sample size. On the contrary, unsupervised methods rely on handcrafted features to detect tube-like structures such as vessels. However, those methods require complex pipelines involving several hyper-parameters and design choices rendering the procedure sensitive, dataset-specific, and not generalizable. Also, unsupervised methods usually underperform supervised methods. We propose a self-supervised method with a limited number of hyper-parameters that is generalizable. Our method uses tube-like structure properties, such as connectivity, profile consistency, and bifurcation, to introduce inductive bias into a learning algorithm. To model those properties, we generate a vector field that we refer to as a flow. Our experiments on various datasets in 2D and 3D show that our method reduces the gap between supervised and unsupervised methods. Unlike generic self-supervised methods, the learned features are transferable for supervised approaches, which is essential when the number of annotated data is limited.
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