Segmentation and optical flow estimation in cardiac CT sequences based on a spatiotemporal PDM with a correction scheme and the Hermite transform

Optical flow estimation of coronary angiography sequences based on semi-supervised learning

Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce. The current optical flow estimation models trained on these non-medical datasets, such as KITTI, Sintel, and FlyingChairs are unsuitable for medical images. In this work, we propose a semi-supervised learning mechanism to estimate the optical flow of coronary angiography. Our proposed method only needs the original medical images, segmentation results of regions of interest, and pre-trained models based on other optical flow datasets to train a new optical flow estimation model suitable for medical images. First, we use the coronary segmentation results to perform image enhancement processing on the coronary vascular region to improve the image contrast between the vascular region and the surrounding tissues. Then, we extract the high-precision optical flow of coronary arteries based on the coronary-enhanced images and the pre-trained optical flow estimation model. After estimating the optical flow, we take it and its corresponding original coronary angiography images as the training dataset to train the optical flow estimation network. Furthermore, we generate a large-scale synthetic Flying-artery dataset based on coronary artery segmentation results and original coronary angiography images, which is used to improve and evaluate the accuracy of optical flow estimation for coronary angiography. The experimental results on the coronary angiography datasets demonstrate that our proposed method can significantly improve the optical flow estimation accuracy of coronary angiography sequences compared with other methods.

3D Hermite Transform Optical Flow Estimation inLeft Ventricle CT Sequences

Heart diseases are the most important causes of death in the world and over the years, thestudy of cardiac movement has been carried out mainly in two dimensions, however, it is important toconsider that the deformations due to the movement of the heart occur in a three-dimensional space.The 3D + t analysis allows to describe most of the motions of the heart, for example, the twistingmotion that takes place on every beat cycle that allows us identifying abnormalities of the heartwalls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiacmovement. In this work, we developed a new approach to determine the cardiac movement inthree dimensions using a differential optical flow approach in which we use the steered Hermitetransform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model ofthe human vision system (HVS). Our proposal was tested in complete cardiac computed tomography(CT) volumes ( 3D + t), as well as its respective left ventricular segmentation. The robustness tonoise was tested with good results. The evaluation of the results was carried out through errors inforwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained(interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, theinterpolation errors and normalized interpolation errors are very close and below the values reportedin ground truth flows. In the case of the 3D algorithm, the results were compared with another similarmethod in 3D and the interpolation errors remained below 0.1. These results of interpolation errorsfor complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a seriesof graphs are observed where the characteristic of contraction and dilation of the left ventricle isevident through the representation of the 3D optical flow.

A 3D Hermite-based multiscale local active contour method with elliptical shape constraints for segmentation of cardiac MR and CT volumes

Analysis of cardiac images is a fundamental task to diagnose heart problems. Left ventricle (LV) is one of the most important heart structures used for cardiac evaluation. In this work, we propose a novel 3D hierarchical multiscale segmentation method based on a local active contour (AC) model and the Hermite transform (HT) for LV analysis in cardiac magnetic resonance (MR) and computed tomography (CT) volumes in short axis view. Features such as directional edges, texture, and intensities are analyzed using the multiscale HT space. A local AC model is configured using the HT coefficients and geometrical constraints. The endocardial and epicardial boundaries are used for evaluation. Segmentation of the endocardium is controlled using elliptical shape constraints. The final endocardial shape is used to define the geometrical constraints for segmentation of the epicardium. We follow the assumption that epicardial and endocardial shapes are similar in volumes with short axis view. An initialization scheme based on a fuzzy C-means algorithm and mathematical morphology was designed. The algorithm performance was evaluated using cardiac MR and CT volumes in short axis view demonstrating the feasibility of the proposed method.