Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling.
Marco BolognaSusanna MiglioriEros MontinRajiv RampatGabriele DubiniFrancesco MigliavaccaLuca MainardiClaudio ChiastraPublished in: PloS one (2019)
This study proposes a segmentation algorithm for the detection of lumen contours and stent struts in low quality OCT images of patients treated with polymeric bioresorbable scaffolds. The segmentation results were successfully used for the reconstruction of one coronary artery model that included a bioresorbable scaffold geometry for computational fluid dynamics analysis.
Keyphrases
- deep learning
- coronary artery
- convolutional neural network
- optical coherence tomography
- tissue engineering
- machine learning
- pulmonary artery
- diabetic retinopathy
- coronary artery disease
- drug delivery
- optic nerve
- cancer therapy
- heart failure
- drug release
- label free
- neural network
- pulmonary hypertension
- pulmonary arterial hypertension
- transcatheter aortic valve replacement