Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications

Reconstruction of Partially Broken Vascular Structures in X-Ray Images via Vesselness-Loss-Based Multi-Scale Generative Adversarial Networks

Coronary artery procedures are primarily performed based on X-ray angiography images. However, coronary arteries in X-ray images are often partially broken, complicating diagnoses and procedures owing to lack of visibility. In this paper, we propose a fully automatic method to restore locally broken parts of coronary arteries in X-ray images without using any external information, such as computed tomography images. To this end, we design a new multi-scale generative adversarial network and a vesselness-loss function. The proposed method is optimized for focus on elongated structures and can be utilized in various clinical applications. The proposed method is evaluated and compared with four other existing methods using the performance metrics, PSNR, MSE, and SSIM, and the result shows 34.3, 0.18, and 0.91 averages, respectively for each metric. Based on the performance result, the blocked regions are plausibly reconstructed into such original shapes of blood vessels, which can aid in image-based guiding catheter manipulation during coronary artery procedures. Eventually, the proposed method can be utilized in various clinical applications, e.g., image-based planning and guidance of coronary procedures and prior simulation of results

Test result using 8 Rotterdam Training dataset.

<p>It shows detailed values for each vessel. (sc: score.).</p

Average overlap per Test dataset.

<p>Average overlap per Test dataset.</p

Exhaustive search for initial direction of vessel using cylinder model.

<p>Exhaustive search for initial direction of vessel using cylinder model.</p

Examples of coronary artery structure found by the proposed method.

<p>Examples of coronary artery structure found by the proposed method.</p

System workflow.

<p>System workflow.</p

Standard deviation and p-values for all parameters OV, OF, OT (Table 3).

<p>Standard deviation and p-values for all parameters OV, OF, OT (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156837#pone.0156837.t003" target="_blank">Table 3</a>).</p

Average accuracy per Test dataset.

<p>Average accuracy per Test dataset.</p