Automatic branch detection of the arterial system from abdominal aortic segmentation

• Published in: Medical & biological engineering & computing 2022-09, Vol.60 (9), p.2639-2654
• Main Authors: Riffaud, Sébastien, Ravon, Gwladys, Allard, Thibault, Bernard, Florian, Iollo, Angelo, Caradu, Caroline
• Format: Article
• Language: English
• Subjects: Abdomen; Algorithms; Aorta; Aortic aneurysms; Arteries; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Blood clots; Branches; Cardiology and cardiovascular system; Combinatorics; Computer Applications; Computer Science; Coronary vessels; Diameters; Feature extraction; Human health and pathology; Human Physiology; Imaging; Life Sciences; Mathematics; Medical Imaging; Original Article; Radiology; Renal artery; Segmentation; Similarity measures; Tomography; Veins & arteries
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-022-02603-2

We present a new method to automatically identify the different arteries present in an abdominal aortic segmentation. In this approach, the arterial system is first represented by a vascular tree, extracted from the segmentation and containing the topologic and geometric features (branch position, branch direction, branch length, branch diameter) of the arterial system. Then, the branches of the vascular tree are matched with the main arteries originating from the aorta: celiac artery, superior mesenteric artery, renal arteries and common iliac arteries. This match is determined by maximizing a similarity measure between the different branches and corresponding arteries. We evaluate this method on 239 segmentations obtained from 102 different patients. The results demonstrate the accuracy of the proposed method, capable of delivering an error of less than 2.5% for the identification of the celiac and superior mesenteric arteries, 8.4% for the renal arteries, and 2.1% for the common iliac arteries. Graphical abstract