VesselKnife – software for the analysis of tubular structures in biomedical images

Background and objective

Accurate segmentation and modeling of blood vessels are critical for understanding vascular anatomy and pathology. VesselKnife is specialized software developed to address challenges in radius estimation and 3D reconstruction, particularly for thin vessel segments and complex geometries. This study evaluates the software's accuracy and versatility in medical imaging applications.

Methods

VesselKnife incorporates advanced techniques based on multiscale vesselness functions and principal component analysis to estimate vessel radii and characterize lumen orientation. The algorithms were validated using synthetic 3D images of tubular structures with varying radii and noise levels, as well as real medical imaging data from MRI and CT. Quantitative performance metrics, including relative error, precision, and robustness to noise, were assessed.

Results

The software achieved high accuracy in synthetic data experiments, with radius estimation errors ranging from 2.3 % to 5.7 % for small vessels and up to 2.9 % for larger vessels. It demonstrated robustness to noise and provided reliable segmentation for thin vessel segments. In real imaging scenarios, VesselKnife was effective in modeling cerebral and renal vasculature. Additionally, the tool was applied to confocal microscopy and scaffold modeling, illustrating its adaptability to diverse imaging modalities.

Conclusions

VesselKnife addresses critical gaps in vascular modeling with precise radius estimation and robust performance across varying imaging conditions. The release of a synthetic 3D image database promotes reproducibility and further research. VesselKnife is a reliable tool for researchers and practitioners in biomedical imaging, with potential applications extending beyond vascular studies.