Abstract
Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics, such as the changes of cerebral blood flow, perfusion or oxygen concentration in arteries and veins under different pathological and physiological conditions. Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio. In this work, we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging (LSI) and optical intrinsic signals (OIS) imaging. The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis (eLASCA) algorithm. The vascular pattern was extracted and segmented using region growing algorithm from the eLASCA-based LSI. Meanwhile, OIS images were acquired alternatively with 630 and 870 nm to obtain an oxyhemoglobin concentration map over cerebral cortex. Then the separation of arteries and veins was accomplished by Otsu threshold segmentation algorithm based on the OIS information and segmentation of LSI. Finally, the segmentation and separation performances were assessed using area overlap measure (AOM). The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases, as well as in clinical intraoperative monitoring.
Highlights
Cortical vessel separation is highly valuable in cerebral hemodynamics study, and plays an important role in clinical diagnostic and therapeutic management of a variety of vascular diseases, e.g., cerebral hemorrhage, stroke, aneurysm, etc.[1]
We proposed a separation method using dual-modal optical imaging techniques, i.e., laser speckle imaging (LSI) and optical intrinsic signals (OIS), to achieve a high spatial resolution for vessel-type separation
(2) Image Enhancement The laser speckle contrast images were enhanced by utilizing enhanced laser speckle contrast analysis algorithm based upon monotonic point transformation (MPT), which improved the CBF visualization and reserved the CBF variability
Summary
Cortical vessel separation is highly valuable in cerebral hemodynamics study, and plays an important role in clinical diagnostic and therapeutic management of a variety of vascular diseases, e.g., cerebral hemorrhage, stroke, aneurysm, etc.[1]. Multiscale matchedlter and a single Gaussian model were employed to acquire di®erent vessel types.[5] Zhong et al applied a method of independent component analysis (ICA) to OIS images and separated the arterial and venous regions. They constructed a feature vector combining heartbeat and respiration features for the fuzzy c-means clustering method and determined the vessel types using morphological intersection points.[6] Hu et al identied the vesseltype based on the phenomenon that the spectral distribution of OIS was di®erent between arterial and venous vessels.[7] Vanzetta et al identied di®erent cortical microvascular compartments in anesthetized cats by the ratio image of two wavelengths at 540 and 560 nm, which were corresponding to the peaks of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR), respectively. We used area overlap measure (AOM) to evaluate the segmentation and separation performance
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