Abstract
Aim: Neurovascular abnormalities are extremely complex, due to the multitude of factors acting simultaneously on cerebral hemodynamics. Cerebral Arteriovenous Malformation (CAVM) hemo-dynamic in one of the vascular abnormality condition results changes in the vessels structures and hemodynamics in blood vessels. The challenge is segmenting accurate vessel region to measure hemodynamics of CAVM patients. The clinical procedure is in-vivo method to measure hemodynamics. The catheter-based procedure is difficult, as it is sometimes difficult to reach vessels sub-structures. Methods: In this paper, we have proposed adaptive vessel segmentation based on threshold technique for CAVM patients. We have compared different adaptive methods for vessel segmentation of CAVM structures. The sub-structures are modeled using lumped model to measure hemodynamics non-invasively. Results: Twenty-three CAVM patients with 150 different vessel locations of DSA datasets were studied as part of the adaptive segmentation. 30 simulated data has been evaluated for more than 150 vessels locations for sub-segmentation of vessels. The segmentation results are evaluated with accuracy of 93%. The computed p-value is smaller than the significance level 0.05. Conclusion: The adaptive segmentation using threshold based produces accurate vessel segmentation, results in better accuracy of hemodynamic measurements for DSA images for CAVM patients. The proposed adaptive segmentation helps clinicians to measure hemodynamic non-invasively for the segmented sub-structures of vessels.
Highlights
Cerebral Arteriovenous Malformation (CAVM) is one of the neurovascular disease conditions, which changes the cerebral angioarchitecture and hemodynamics changes in the flow and pressure level in blood vessels
Twenty-three CAVM patients obtained from Cathlab, Kasturba Medical College (KMC) Manipal from the study population
Twenty-three CAVM patients with 150 different vessel location of Digital Subtraction Angiogram (DSA) datasets were studied as part of the adaptive modeling and 30 simulated data are created with equivalent complexity of DSA, has been evaluated for more than 150 vessels locations for sub-segmentation of vessels
Summary
Cerebral Arteriovenous Malformation (CAVM) is one of the neurovascular disease conditions, which changes the cerebral angioarchitecture and hemodynamics changes in the flow and pressure level in blood vessels. CAVM vessels are made of tangled abnormal vessels, which form a complex vessels structure called Nidus. The invasive procedure to measure hemodynamics near Nidus is risky. The studies show that various phase of acquisition of DSA images is used for analysis of vessel segmentation in cerebrovascular patients [1], but limited to complex structures. The literature shows the recursive tracking techniques to detect the vessel network, which has limitation [2] such as handling of structural variations. The author Nong Sang [3] studied the vessel segmentation of DSA image. Drawback of his study is thresholding method for non-overlapping sub images is not considered
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