DCNN-based automatic segmentation and quantification of aortic thrombus volume: influence of the training approach

  • Authors
  • López-Linares K., Kabongo L., Lete N., Maclair G., Ceresa M., García-Familiar A., Macía I., González Ballester M.A
  • UPF authors
  • CERESA ., MARIO; GONZALEZ BALLESTER, MIGUEL ANGEL;
  • Type
  • Scholarly articles
  • Journal títle
  • Lecture Notes in Computer Science / Artificial Intelligence
  • Publication year
  • 2017
  • Volume
  • 10552
  • Pages
  • 29-38
  • ISSN
  • 0302-9743
  • Publication State
  • Published
  • Abstract
  • Computerized Tomography Angiography (CTA) based assessment of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential during follow-up to evaluate the progress of the patient along time, comparing it to the pre-operative situation, and to detect complications. In this context, accurate assessment of the aneurysm or thrombus volume pre- and post-operatively is required. However, a quantifiable and trustworthy evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose an automatic pipeline for thrombus volume assessment, starting from its segmentation based on a Deep Convolutional Neural Network (DCNN) both pre-operatively and post-operatively. The aim is to investigate several training approaches to evaluate their influence in the thrombus volume characterization.
  • Complete citation
  • López-Linares K., Kabongo L., Lete N., Maclair G., Ceresa M., García-Familiar A., Macía I., González Ballester M.A. DCNN-based automatic segmentation and quantification of aortic thrombus volume: influence of the training approach. Lecture Notes in Computer Science / Artificial Intelligence 2017; 10552( ): 29-38.
Bibliometric indicators
  • 2 times cited Scopus
  • 1 times cited WOS
  • Índex Scimago de 0.295(2017)