DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image

TL;DR

DeformCL introduces a continuous deformable centerline representation for 3D vessel extraction, improving connectivity, noise robustness, and clinical relevance over traditional discrete methods.

Contribution

The paper proposes DeformCL, a novel deformable centerline-based representation for vessel segmentation, with a cascaded training pipeline and validated on multiple datasets.

Findings

Outperforms existing methods on four 3D vessel datasets

Provides more natural connectivity and noise robustness

Enables effective clinical visualization

Abstract

In the field of 3D medical imaging, accurately extracting and representing the blood vessels with curvilinear structures holds paramount importance for clinical diagnosis. Previous methods have commonly relied on discrete representation like mask, often resulting in local fractures or scattered fragments due to the inherent limitations of the per-pixel classification paradigm. In this work, we introduce DeformCL, a new continuous representation based on Deformable Centerlines, where centerline points act as nodes connected by edges that capture spatial relationships. Compared with previous representations, DeformCL offers three key advantages: natural connectivity, noise robustness, and interaction facility. We present a comprehensive training pipeline structured in a cascaded manner to fully exploit these favorable properties of DeformCL. Extensive experiments on four 3D vessel segmentation datasets demonstrate the effectiveness and superiority of our method. Furthermore, the visualization of curved planar reformation images validates the clinical significance of the proposed framework. We release the code in https://github.com/barry664/DeformCL