Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images

AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS: The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research

Calcification detection of coronary artery disease in intravascular ultrasound image: Deep feature learning approach

Coronary artery disease (CAD) is part of the non-communicable disease (NCD) in cardiovascular disease (CVD). The blood vessel area became narrow when the calcification with the plaque embedded in the coronary artery inner wall. The radiologists and medical practitioners used visual inspection to detect calcification on IVUS image. The presence of calcification will not be able to do the measurement to calculate the maximum diameter and the maximum area for the patient coronary artery either before treatment or after treatment. More than 100 frames per patient is needed to analyse the location of the calcification. In this study, our aim is to detect the presence and the absence of the calcification in the coronary artery using intravascular ultrasound (IVUS) images with catheter frequency of 20MHz. The IVUS images used were the original Cartesian coordinate image and the polar reconstructed coordinate image. In this study, three types of convolutional neural network (CNN) using Directed Acyclic Graph networks, were used together with five types of classifiers. The dataset used to demonstrate our framework is Dataset B from MICCAI Challenge 2011 that consists of 2175 coronary artery disease IVUS image where 530 are IVUS images with calcification and 1645 are IVUS images without calcification. The cross validation for testing and training, the k-fold value used was 2, 3, 5 and 10. The performance measures for the ResNet-50, the ResNet-101 and the Inception-V3 model shows an excellent result using support vector machine classifier and discriminant analysis for both types of images. A better improvement using polar reconstructed coordinate image when using decision tree classifier and Naïve Bayes classifier whilst ResNet-101 architecture shows an excellent performance measure when applying images polar reconstructed images when using k-nearest neighbor classifier. However, Naïve Bayes classifier has an excellent result when using Inception-V3 architecture

Missing Surface Estimation Based on Modified Tikhonov Regularization: Application for Destructed Dental Tissue

Estimation of missing digital information is mostly addressed by 1- or 2-D signal processing methods; however, this problem can emerge in multi-dimensional data including 3-D images. Examples of 3-D images dealing with missing edge information are often found using dental micro-CT, where the natural contours of dental enamel and dentine are partially dissolved or lost by caries. In this paper, we present a novel sequential approach to estimate the missing surface of an object. First, an initial correct contour is determined interactively or automatically, for the starting slice. This contour information defines the local search area and provides the overall estimation pattern for the edge candidates in the next slice. The search for edge candidates in the next slice is performed in the perpendicular direction to the obtained initial edge in order to find and label the corrupted edge candidates. Subsequently, the location information of both initial and nominated edge candidates are transformed and segregated into two independent signals (X-coordinates and Y-coordinates) and the problem is changed into error concealment. In the next step, the missing samples of these signals are estimated using a modified Tikhonov regularization model with two new terms. One term contributes in the denoising of the corrupted signal by defining an estimation model for a group of mildly destructed samples, and the other term contributes in the estimation of the missing samples with the highest similarity to the samples of the obtained signals from the previous slice. Finally, the reconstructed signals are transformed inversely to edge pixel representation. The estimated edges in each slice are considered as initial edge information for the next slice, and this procedure is repeated slice by slice until the entire contour of the destructed surface is estimated. The visual results as well as quantitative results (using both contour-based and area-based metrics) for seven image data sets of tooth samples with considerable destruction of the dentin-enamel junction demonstrates that the proposed method can accurately interpolate the shape and the position of the missing surfaces in computed tomography images in both two and 3-D (e.g., 14.87 ± 3.87 $\mu \text{m}$ of mean distance (MD) error for the proposed method versus 7.33 ± 0.27 $\mu \text{m}$ of MD error between human experts and 1.25± ~ 0 % error rate (ER) of the proposed method versus 0.64± ~

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and theidentification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on theK-means algorithm and the mean roundness to identify the region corresponding to the potential lumen.An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the areawith the potential lumen regions. Additionally, an active contour model is applied to refine the contourof the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed methodwere compared against manual delineations made by two experts in 326 IVUS images of the coronaryartery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference andDice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324IVUS images successfully segmented. Additionally, a comparison with the studies found in the literatureshowed that the proposed method is slight better than the majority of the related methods that havebeen proposed. Hence, the new automatic segmentation method is shown to be effective in detecting thelumen in IVUS images without using complex solutions and user interaction