Laser speckle contrast imaging(LSCI)is a noninvasive,label-free technique that allows real-time investigation of the microcirculation situation of biological tissue.High-quality microvascular segmentation is critical ...Laser speckle contrast imaging(LSCI)is a noninvasive,label-free technique that allows real-time investigation of the microcirculation situation of biological tissue.High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics.However,achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology.In addition,supervised learning methods heavily rely on high-quality labels for accurate segmentation results,which often necessitate extensive labeling efforts.Here,we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing.Furthermore,we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets,even when confronted with unfamiliar data.Importantly,the dice similarity coefficient exceeded 85%in a rat experiment.Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations.This would greatly benefit future research in life and medicine.展开更多
基金supported by grants fromthe State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(2023XAKJ0101031)National Natural Science Foundation of China(81971665)+8 种基金Natural Science Foundation of Fujian Province(2021J011366)Medical and Health Guidance Project of Xiamen(3502Z20214ZD1016)Xiamen Health High-Level Talent Training Program,Ningxia Hui Autonomous Region Key Research and Development Program(2022BEG03127)Fundamental Research Funds for the Central Universities of China(20720210117)Fujian Province Science and Technology Plan Guiding Project(2022Y0002)National Natural Science Foundation of China(62005048)Natural Science Foundation of Fujian Province(2020J01158)Ministry of Education Industry-university Cooperative Education Project(220606053295218)XMU Undergraduate Innovation and Entrepreneurship Training Programs(2023X805,2023X808,2023Y1109).
文摘Laser speckle contrast imaging(LSCI)is a noninvasive,label-free technique that allows real-time investigation of the microcirculation situation of biological tissue.High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics.However,achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology.In addition,supervised learning methods heavily rely on high-quality labels for accurate segmentation results,which often necessitate extensive labeling efforts.Here,we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing.Furthermore,we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets,even when confronted with unfamiliar data.Importantly,the dice similarity coefficient exceeded 85%in a rat experiment.Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations.This would greatly benefit future research in life and medicine.
