Semi-automatic segmentation and detection of aorta dissection wall in MDCT angiography

• Published in: Medical image analysis Vol. 18; no. 1; pp. 83 - 102
• Main Authors: Krissian, Karl, Carreira, Jose M., Esclarin, Julio, Maynar, Manuel
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2014
• Subjects: Algorithms; Aneurysm, Dissecting - diagnostic imaging; Aorta; Aortic Aneurysm - diagnostic imaging; Aortography - methods; Artificial Intelligence; Computed tomography; Dissection wall; Humans; Multidetector Computed Tomography - methods; Pattern Recognition, Automated - methods; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; User-Computer Interface; Vessel segmentation; Dissection wall; Aorta; Vessel segmentation; Computed tomography
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2013.09.004

[Display omitted] •A method for semi-automatic segmentation of the aortic dissection wall is proposed.•The centerlines of the aorta and its main branches are extracted semi-automatically.•We segment the outer vessel wall using a geodesic levelset framework.•An algorithm is proposed to extract the dissection wall as a 3D mesh.•Results on five MDCT datasets show an mean absolute error of 0.34mm. Aorta dissection is a serious vascular disease produced by a rupture of the tunica intima of the vessel wall that can be lethal to the patient. The related diagnosis is strongly based on images, where the multi-detector CT is the most generally used modality. We aim at developing a semi-automatic segmentation tool for aorta dissections, which will isolate the dissection (or flap) from the rest of the vascular structure. The proposed method is based on different stages, the first one being the semi-automatic extraction of the aorta centerline and its main branches, allowing an subsequent automatic segmentation of the outer wall of the aorta, based on a geodesic level set framework. This segmentation is then followed by an extraction the center of the dissected wall as a 3D mesh using an original algorithm based on the zero crossing of two vector fields. Our method has been applied to five datasets from three patients with chronic aortic dissection. The comparison with manually segmented dissections shows an average absolute distance value of about half a voxel. We believe that the proposed method, which tries to solve a problem that has attracted little attention to the medical image processing community, provides a new and interesting tool to isolate the intimal flap that can provide very useful information to the clinician.