Purpose: In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. As such, developing accurate segmentation of vascular structures and quantification of coronary arterial stenosis in invasive coronary angiograms (ICA) is necessary.Approach: A multi-input and multiscale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. The proposed model generated a refined prediction map with the following two training stages: (i) stage I coarsely segmented the major coronary arteries from preprocessed single-channel ICAs and generated the probability map of arteries; and (ii) during the stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation result. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level.Results: Experimental results demonstrated that the proposed method achieved an average Dice similarity coefficient of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patients. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043.Conclusions: Our proposed approach has great promise for clinical use and could help physicians improve diagnosis and therapeutic decisions for CAD.
Gated myocardial perfusion SPECT (MPS) is widely used to assess the left ventricular (LV) function. Its performance relies on the accuracy of segmentation on LV cavity. We propose a novel machine-learningbased method to automatically segment LV cavity and measure its volume in gated MPS imaging. To perform end-to-end segmentation, a multi-label V-Net is used to build the network architecture. The network segments a probability map for each heart contour (epicardium, endocardium and myocardium). To evaluate the accuracy of segmentation, we retrospectively investigated gated MPS images from 32 patients. The LV cavity was automatically segmented by the proposed method, and compared to manually outlined contours, which were taken as the ground truth. The derived LV cavity volumes were extracted from both ground truth and results of proposed method for comparison and evaluation. The mean DSC, sensitivity and specificity of the contours delineated by our method are all above 0.9 among all 32 patients and 8 phases. The correlation coefficient of the LV cavity volume between ground truth and results produced by the proposed method is 0.910±0.061, and the mean relative error of LV cavity volume among all patients and all phases is - 1.09±3.66 %. These results indicate that the proposed method accurately quantifies the changes in LV cavity volume during the cardiac cycle. It also demonstrates the potential of our learning-based segmentation methods in gated MPS imaging for clinical use.
KEYWORDS: Veins, Lead, Image fusion, CRTs, Single photon emission computed tomography, 3D modeling, Image registration, 3D image processing, 3D acquisition
Multi-modality image fusion of 3D coronary venous anatomy from fluoroscopic venograms with left ventricular (LV) epicardial surface from single-photon emission computed tomography (SPECT) myocardial perfusion image (MPI) can provide both LV physiological information and venous anatomy for guiding CRT LV lead placement. However, it is difficult to match the time points between MPI and venograms because of heart beating and thus image acquisition of the different cardiac frames, which affects the accuracy of 3D fusion. To address this issue, this study introduces a scale ratio iterative closest point (S-ICP) algorithm to non-rigidly fuse images from two different modalities. Three steps, including the image reconstruction, image registration, and image overlay were implemented to complete the images fusion. First, the 3D fluoroscopic venous anatomy and SPECT LV epicardial surface were reconstructed. Second, a landmark-based registration method was performed as an initial registration of S-ICP. With the initialization, the S-ICP algorithm with a preset scale range completed a fine registration for SPECT-vein fusion. Third, the registered venous anatomy was overlaid onto the SPECT LV epicardial surface. Moreover, in order to validate the accuracy of the fusion, 3D CT venous anatomy was manually fused with the same SPECT LV epicardial surface, and then the distance-based mismatch errors between fluoroscopic veins and CT veins were evaluated. Five patients were enrolled. As a result, the overall mismatch error was 5.6±4.1mm, which is smaller than the pixel size of SPECT images (6.4mm).
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