Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
This addresses the challenge of reducing reliance on expert annotations for vessel segmentation in clinical applications, though it is incremental as it builds on existing self-supervised and unsupervised approaches.
The paper tackles the problem of vessel segmentation in medical imaging by proposing a self-supervised method that uses flow-based consistencies to learn vessel-relevant features from unlabeled data, achieving better performance than unsupervised methods on various 2D and 3D public datasets.
Vessel segmentation 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. We propose a self-supervised method with a limited number of hyper-parameters that is generalizable across modalities. 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 public datasets in 2D and 3D show that our method performs better than unsupervised methods while learning useful transferable features from unlabeled data. Unlike generic self-supervised methods, the learned features learn vessel-relevant features that are transferable for supervised approaches, which is essential when the number of annotated data is limited.