Shape prior generation and geodesic active contour interactive iterating algorithm (SPACIAL): fully automatic segmentation for 3D lumen in intravascular optical coherence tomography images

Abstract

Fully automatic lumen segmentation in intravascular optical coherence tomography (OCT) images can assist physicians in quickly estimating the health status of vessels. However, OCT images are usually degraded by residual blood, catheter walls, guide wire artifacts, etc., which significantly reduce the quality of segmentation. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm named SPACIAL: Shape Prior generation and geodesic Active Contour Interactive iterAting aLgorithm, which is guided by an adaptively generated shape prior. Methods: In this framework, the active contour evolves under the guidance of shape prior, while the shape prior is automatically and adaptively generated based on the active contour. The active contour and the shape prior interactively iterate each other, which can generate the adaptive shape prior and consequently lead to accurate segmentation results. In addition, a fast algorithm is introduced to accelerate the segmentation in 3D images. Results:The validity of the model is verified in 3240 images from 12 OCT pullbacks. The experimental results show satisfactory segmentation accuracy and time efficiency: the average Dice coefficient of SPACIAL is 93.6(2.4)%, and 5.7 times faster than that of the classical level set method. Conclusion:The proposed SPACIAL can quickly and efficiently perform accurate lumen segmentation on low quality OCT images, which is of great importance to cardiovascular disease diagnosis . The SPACIAL method shows great potential in clinical applications.

Automatic lumen and anatomical layers segmentation in IVOCT images using meta learning

“…These methods can be divided into classification-based methods and regression-based methods. The classification-based methods utilize fully convolutional networks (FCN) to classify each pixel of IVOCT images for obtaining the lumen and anatomical layers [19][20][21][22][23][24][25]. The regressionbased methods directly regress coordinates of lumen contour and radial distances from the catheter centroid to the lumen using IVOCT images in polar view [26].…”