Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
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...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
13.01.2021
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2101.05145 |