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Vessel Structure Extraction using Constrained Minimal Path Propagation.
Metadata
Journalartificial intelligence in medicine4.383Date
2020 Apr 25
5 months ago
Type
Research Support, Non-U.S. Gov't
Journal Article
Volume
2020-05 / 105 : 101846
Author
Yang G 1, Lv T 2, Shen Y 3, Li S 4, Yang J 5, Chen Y 6, Shu H 1, Luo L 1, Coatrieux JL 7
Affiliation
  • 2. Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.
  • 3. Laboratory of Image Science and Technology, Southeast University, Nanjing, China.
  • 4. Department of Medical Imaging, Western University, London, ON, Canada; Digital Image Group of London, London, ON, Canada.
  • 5. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, China. Electronic address: [email protected]
  • 6. Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France; Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China. Electronic address: [email protected]
  • 7. Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.
Doi
PMIDMESH
Abstract
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.
Keywords: Minimal path approach backtracking segmentation. vascular structures
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Artif Intell Medartificial intelligence in medicine
Metadata
LocationNetherlands
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