interactive Image Segmentation With Transformers

Optical coherence tomography (OCT) is widely used in high-resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessel lumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSP-Regularizer is applied to improve the optimization scheme used in Mask-RCNN for vessel lumen segmentation. Experimental results show that superior performance is achieved with SSP-Regularizer, indicating its potentials in OCT imagery and optimization schemes.