Using Watershed and Multimodal Data for Vessel Segmentation: Application to the Superior Sagittal Sinus

Abstract

Magnetic resonance angiography (MRA) provides 3-dimensional data of vascular structures by finding the flowing blood signal. Classically, algorithms dedicated to vessel segmentation detect the cerebral vascular tree by only seeking the high intensity blood signal in MRA. We propose here to use both cerebral MRA and MRI and to integrate a priori anatomical knowledge to guide the segmentation process. The algorithm presented here uses mathematical morphology tools (watershed segmentation and grey-level operators) to carry out a simultaneous segmentation of both blood signal in MRA and blood and wall signal in MRI. It is dedicated to the superior sagittal sinus segmentation but similar strategies could be considered for segmentation of other vascular structures. The method has been performed on 6 cases composed of both MRA and MRI. The results have been validated and compared to other results obtained with a region growing algorithm. They tend to prove that this method is reliable even when the vascular signal is inhomogeneous or contains artefacts.