Vessel Structure Extraction using Constrained Minimal Path Propagation

• Published in: Artificial intelligence in medicine Vol. 105; p. 101846
• Main Authors: Yang, Guanyu, Lv, Tianling, Shen, Yunpeng, Li, Shuo, Yang, Jian, Chen, Yang, Shu, Huazhong, Luo, Limin, Coatrieux, Jean-Louis
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020; Elsevier
• Subjects: backtracking; Bioengineering; Life Sciences; Minimal path approach; segmentation; vascular structures; Minimal path approach; segmentation; vascular structures; backtracking
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2020.101846

•A strategy for a complete vessel extraction is provided under the framework of minimal path propagation.•Two constraints (potential constraint and radius constraint) are devised to provide efficient vessel extraction.•The close loop problem in the MPP-BT algorithm is solved by applying a local MPP-BT operation. Minimal path method has been widely recognized as an efficient tool for extracting vascular structures in medical imaging. In a previous paper, a method termed minimal path propagation with backtracking (MPP-BT) was derived to deal with curve-like structures such as vessel centerlines. A robust approach termed CMPP (constrained minimal path propagation) is here proposed to extend this work. The proposed method utilizes another minimal path propagation procedure to extract the complete vessel lumen after the centerlines have been found. Moreover, a process named local MPP-BT is applied to handle structure missing caused by the so-called close loop problems. This approach is fast and unsupervised with only one roughly set start point required in the whole process to get the entire vascular structure. A variety of datasets, including 2D cardiac angiography, 2D retinal images and 3D kidney CT angiography, are used for validation. A quantitative evaluation, together with a comparison to recently reported methods, is performed on retinal images for which a ground truth is available. The proposed method leads to specificity (Sp) and sensitivity (Se) values equal to 0.9750 and 0.6591. This evaluation is also extended to 3D synthetic vascular datasets and shows that the specificity (Sp) and sensitivity (Se) values are higher than 0.99. Parameter setting and computation cost are analyzed in this paper.