Research On Automatic Segmentation Of Calcified Plaques By Intravascular OCT

Coronary artery disease（CAD）is among the leading causes of death across the globe.Every year,more than 2 million people receive stent implantation with percutaneous coronary intervention（PCI）for treating CAD.Coronary calcification is a strong indicator of coronary artery disease and a key determinant of the outcome of percutaneous coronary intervention.Thus,accurate evaluation of coronary calcification is critical to planning a PCI strategy.Intravascular optical coherence tomography（IVOCT）is a powerful imaging technology for assessing CAD and planning PCI and can better assess the vessel lumen boundary and plaque phenotype.However,quantitative analysis of CAC remains a challenge during PCI.Manual detection is the current approach but is tedious,expensive,time-consuming,impractical in larger studies,and it introduces interobserver variability.Hence,it is critical to have a fast,accurate,and ideally fully automated calcification analysis method in the procedure room for facilitating timely treatment decision planning.This thesis proposes a fully automated method to segment and quantify coronary calcification in IVOCT images based on convolutional neural networks（CNN）,including the following main contents:Firstly,this thesis segments all possible calcified plaques from IVOCT clinical pullbacks using a spatial-temporal encoder-decoder network,also known.The network combining 2D and 3D convolutions extracted in-plane and through-plane features and provided pixel-wise prediction for IVOCT volumes in the Cartesian coordinates.To remove false positives,we further proposed a CNN-based region of interest（ROI）classification method using Dense Net.Secondly,this thesis proposes a novel and powerful data augmentation method with mechanisms of restitching,rotational resampling,and expansion/contraction based on the helical scanning pattern,catheter rotational non-uniformity and interferometric imaging principle of IVOCT,and can naturally create realistic images for training deep neural nets.Finally,this thesis evaluates the method comprehensively using pixel-level,imagelevel,and lesion-level metrics and assesses the accuracy of computing clinically significant metrics including calcification scores for facilitating stent deployment.13844 IVOCT images with 2627 calcification slices from 45 clinical OCT pullbacks were collected and used to train and test the model.The proposed method performed significantly better than existing state-of-the-art 2D and 3D CNN methods,including Seg Net,U-Net,3D U-Net,and V-Net.The data augmentation method improved the Dice similarity coefficient for calcification segmentation from 0.615±0.332 to 0.756±0.222,reaching a human-level inter-observer agreement.The proposed region-based classifier improved image-level calcification classification precision and F1-score from0.725±0.071 and 0.791±0.041 to 0.964±0.002 and 0.883±0.008,respectively.Bland–Altman analysis showed close agreement between manual and automatic calcification measurements.The proposed method based on CNN is valuable for automated assessment of coronary calcification lesions and in-procedure planning of stent deployment.