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Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images\udhas clinical relevance as it provides a full three dimensional (3D) perspective of diseased coronary artery\udsections. Inaccurate segmentation may occur when there are artefacts in the image, resulting from issues\udsuch as inadequate blood clearance. This study proposes a transmittance based lumen intensity enhancement\udmethod that ensures lumen regions only are highlighted. A level-set based active contour method (ACM)\udthat utilizes the local speckle distribution properties of the image, is then employed to drive an imagespecific\udactive contour towards the true lumen boundaries. By utilizing local speckle properties, the intensity\udvariation issues within the image are resolved. This combined approach has been successfully applied to\udchallenging clinical IV-OCT datasets that contains multiple lumens, residual blood flow and its shadowing\udartefact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented.\udThis approach is fast and can perform even when guide-wire boundaries are not easily identified. Lumen\udenhancement also makes it easy to identify vessel side branches. This automated approach is not only able\udto extract the arterial lumen, but also the smaller microvascular lumens that are associated with the vasa\udvasorum and with atherosclerotic plaqueThe work reported in this paper is supported by St Jude Medical UK Limited and NIHR\udLeicester Cardiovascular Biomedical Research Unit, UK. This research used the ALICE\udHigh Performance Computing Facility at the University of Leicester.Peer-reviewedPost-prin

journal of medical imaging

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method