Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features

Abstract

Background Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures. Methods A novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union). Results With hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images. Conclusions The proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.

A Review of Modern Approaches for Coronary Angiography Imaging Analysis

“…As it was stated before, the domain of angiography is intertwined with highly noisy images, patient's motions, varying levels of contrast, thus vessel segmentation and lesion detection tasks require some level of image enhancement, so that visual characteristics of the image can be upgraded. Image enhancement techniques can be split into 2 parts, with the first one being morphological operations such as dilation and erosion [39], [44], [45], and second one being advanced mathematical algorithms like Multiscale Retinex with Color Restoration (MSRCR) [46] and Contrast Limited Histogram Equalization (CLAHE) [47] and even specialized deep neural networks [38].…”

“…where I en is the enhanced image, I the original image, I th the image after the top-hat transform, and I bh the image after the bottom-hat transform. A slightly altered version of this transform includes adjustable weight parameters for I th and I bh [45]. Contrast Limited Adaptive Histogram Equalization (CLAHE) is an image preprocessing algorithm [47], for improved visibility of lesions on mammogram images.…”

“…Another important reason for frame selection is that it reduces that number of input dimensions, which makes the DNN less likely to overfit. Frame selection is usually performed manually [45], [60], [61] either without any additional modalities or in combination with ECG signals, which are collected synchronously with XCA [62]. In order to automate the frame selection process and thus assist radiologists, several algorithmic approaches have been proposed.…”

“…A novel ensemble framework, based on gradient-boosting decision trees [85] and Deep Forest [86] classifiers, developed in [45], is reported to outperform U-net-based algorithms and modern DeepLabV3+ [18]. 21 filter-based features and 16 Dense-Net [77] extracted features are combined into a 37dimensional feature vector for each pixel of the XCA image.…”

Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature

“…Although DL models are widely regarded as state-of-the-art computer solutions for automated decision-making in many different fields, there are some circumstances where conventional ML models or a combination of DL and conventional ML methods offer higher performance or are more affordable than DL methods [ 4 , 28 ]. In a study by Gao et al, for instance, tree-based traditional ML techniques such as gradient boosting machines outperformed neural networks for vessel segmentation on X-ray coronary angiography [ 29 ]. It is also commonly believed that, when applied to tabular data, the same tree-based models are frequently superior to or on par with DL approaches [ 30 ].…”