Fully automated quantification of LV regional wall motion from echocardiograms to detect myocardial infarction

Funding Acknowledgements

Type of funding sources: Private company. Main funding source(s): Ultromics Ltd

Background

Myocardial wall motion analysis from echocardiography allows precise assessment of cardiac contractile function. Strain, which assesses myocardial deformation, has been shown to enable earlier detection of myocardial disease [1]. Current analysis software packages [2] use semi-automated methods to compute strain, which frequently require manual endocardial delineation and iterative contour adjustment based on tracking results, respectively, causing significant variability.

Purpose

We present a fully automated pipeline for tracking left ventricular (LV) wall motion to quantify global and segmental longitudinal strain from 2D echocardiograms, and go on to validate the pipeline with an openly available myocardial infarction (MI) dataset.

Methods

We applied our existing deep learning-based automated contouring method [3] to delineate the endocardial border in the A4C, A2C and A3C views and combined this with spline-based elastic image registration to track LV motion through time. We sampled points from a region of interest initiated from the endocardial border at the end-diastolic (ED) frame, and tracked subsequent motion by recomputing updated positions of all sample points based on each frame‘s displacement field, enabling us to both track the myocardium throughout the cardiac cycle and calculate longitudinal strain relative to the ED frame. The automated endocardial contour was used to regularise the process. The pipeline was independently tested on the HMC-QU dataset [4] which was downloaded from Kaggle and consists of a single cardiac cycle from the A4C view from 160 patients who were diagnosed with an acute MI and underwent echocardiography either prior to percutaneous coronary intervention or within 24 hours of undergoing the procedure; the dataset includes the labels of ED and end-systolic (ES) frames, as well as the presence of an MI in 6 segments excluding the apical cap (Fig 1a), as determined by the consensus of cardiologists from HMC Hospital in Qatar. The Wilcoxon signed-rank test was used to compare peak strain between the MI and non-MI segments; ROC curves were computed to compare the performance of the automatically derived peak longitudinal strain against the MI labels.

Results

Fig 1b shows ROC curves of peak segmental longitudinal strain for detecting MI, with the best performance in the mid-anterolateral segment (AUC 0.84), and a lower performance for basal segments than mid and apical segments, consistent with known variation in clinical practice [5]. Fig 2 shows that peak longitudinal strain computed from our pipeline was statistically significantly more positive in segments with an MI.

Conclusions

We present a fully automated pipeline for calculating segmental strain across a cardiac cycle to identify infarcted segments without any observer variability. Clinical application of this method has the potential to identify and monitor regional myocardial function and benefit patient management. Abstract Figure. Fig1. ROC of peak longitudinal strains  Abstract Figure. Fig2.Boxplot of peak longitudinal strain

Site-specific automated contouring model generalisibiliy enhancement

Funding Acknowledgements

Type of funding sources: None.

Background

Segmentation of cardiac structures in echocardiography is a pre-requisite for accurately assessing cardiac morphology and function. Manual or semi-automated segmentation are both routinely used in clinical practice, although these can be time-consuming, and can introduce high inter- and intra- operator variability resulting in decreased reproducibility. Effective contouring with no manual input has proven to be challenging due to variations in image quality, image noise, motion during the acquisition and the lack of a well-defined geometry.

Methods

This work proposes a coordinate regression method for automated left ventricle (LV) segmentation, presented in Figure 1 (a). The proposed method is based on a modified U-net architecture that outputs the likelihood of coordinates of landmark points. The obtained likelihood heatmaps are converted to 2D coordinates using a differentiable spatial to numerical transform. The model was trained and validated on UK multisite data (1383 subjects) comprising apical 2 and 4 chamber views for both contrast and non-contrast echocardiographic images.

The Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) echocardiographic image segmentation database was used to assess the performance of the proposed method acting as data from a new clinical site. The CAMUS dataset comprises apical 2 and 4 chamber views acquired from 500 patients with manually annotated cardiac structures for end-diastole and end-systole frames. The original CAMUS dataset was split into training (450 patients) and testing (50 patients), with manual contours being available only for the training dataset. Therefore, we used the CAMUS training dataset to both test and improve our model, by using a random sample of 100 studies as an independent testing dataset and the remaining 350 studies were used for retraining the initial model to improve performance for this dataset.

Results

The results obtained on the testing images are presented in Figure 1 (b). When the model was trained using no CAMUS data for the LV segmentation, a mean Dice coefficient of 0.890 and a median of 0.911 was obtained. Including 350 studies with the original 1383 UK dataset and retraining the same model improved the average Dice coefficient to 0.930 and the median to 0.939. The CAMUS dataset authors reported the best average Dice coefficient of 0.924 on the 50 CAMUS testing images, therefore the proposed points regression method introduces a promising alternative to mask-based segmentation models.

Conclusions

In conclusion, the auto-contouring framework has proven to be effective in terms of its performance and ability to generalise to new data. Furthermore, this work highlights the importance of both evaluating model performance on data from new clinical sites and also enhancing model performance.

Abstract Figure.

Contributions to the biology of the hypoxic liver. Note I. Histological and electronmicroscopical aspects of the liver in chronic congestive heart failure

A study was carried out on the histological and ultrastructural aspects of liver fragments harvested from 62 patients with acquired or congenital heart disease before open heart surgery under extracorporeal circulation. Against a background of passive congestion in the pericentrolobular and mediolobular areas, various mitochondrial lesions and dilation of the endoplasmic reticulum with a reduced number of ribosomes, the presence of microbodies, biliary pigments, lipid vacuoles, lysosomol hyperplasia and activation, glycogen depletion could be seen, as well as extensive collagenization of Disse's spaces, fibroblast hyperplasia and Kupffer cell activation. These lesions are more reduced in the periportal zones. In the advanced stages of heart failure, there appeared a cirrhogenic organization due to extensive pericentrolobular and periportal fibrosis.