Semi-automatic vessel detection for challenging cases of peripheral arterial disease

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: March 2021
• DOI: 10.1016/j.compbiomed.2021.104344
• Journal: Computers in Biology and Medicine
• Open Access: yes
• Volume: 133
• Pages: 1 – 14

Abstract

Objectives: Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Pe-ripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifes-tations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data. Methods: We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically clas-sifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization. Results: We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufficient quality for clinical application, with our current clinically established workflow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average specificity and overall accuracy of 99.9%. Conclusions: Compared to the clinical workflow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.compbiomed.2021.104344

BibTeX

@article{Mistelbauer_2021,
  title =      "Semi-automatic vessel detection for challenging cases of
               peripheral arterial disease ",
  author =     "Gabriel Mistelbauer and Anca Morar and R\"{u}diger
               Schernthaner and Andreas Strassl and Dominik Fleischmann and
               Florica Moldoveanu and Eduard Gr\"{o}ller",
  year =       "2021",
  abstract =   "Objectives: Manual or semi-automated segmentation of the
               lower extremity arterial tree in patients with Pe-ripheral
               arterial disease (PAD) remains a notoriously difficult and
               time-consuming task. The complex manifes-tations of the
               disease, including discontinuities of the vascular flow
               channels, the presence of calcified atherosclerotic plaque
               in close vicinity to adjacent bone, and the presence of
               metal or other imaging artifacts currently preclude fully
               automated vessel identification. New machine learning
               techniques may alleviate this challenge, but require large
               and reasonably well segmented training data.  Methods: We
               propose a novel semi-automatic vessel tracking approach for
               peripheral arteries to facilitate and accelerate the
               creation of annotated training data by expert cardiovascular
               radiologists or technologists, while limiting the number of
               necessary manual interactions, and reducing processing time.
               After automatically clas-sifying blood vessels, bones, and
               other tissue, the relevant vessels are tracked and organized
               in a tree-like structure for further visualization. 
               Results: We conducted a pilot (N = 9) and a clinical study
               (N = 24) in which we assess the accuracy and required time
               for our approach to achieve sufficient quality for clinical
               application, with our current clinically established
               workflow as the standard of reference. Our approach enabled
               expert physicians to readily identify all clinically
               relevant lower extremity arteries, even in problematic
               cases, with an average sensitivity of 92.9%, and an average
               specificity and overall accuracy of 99.9%.  Conclusions:
               Compared to the clinical workflow in our collaborating
               hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ±
               6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.   ",
  month =      mar,
  doi =        "10.1016/j.compbiomed.2021.104344",
  journal =    "Computers in Biology and Medicine ",
  volume =     "133",
  pages =      "1--14",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Mistelbauer_2021/",
}