Endocardial activation mapping of human atrial fibrillation

Successful ablation of arrhythmias depends upon interpretation of the mechanism. However, in persistent atrial fibrillation (AF) ablation is currently directed towards the mechanism that initiates paroxysmal AF. We sought to address the hypothesis that atrial activation patterns during persistent AF may help determine the underlying mechanism. Activation mapping of AF wavefronts is labor intensive and often restricted to short time segments in limited atrial locations. RETRO-Mapping was developed to identify uniform wavefronts that occur during AF, and summate all wavefront vectors on to an orbital plot. Uniform wavefronts were mapped using RETRO-Mapping during sinus rhythm, atrial tachycardia, and atrial fibrillation, and validated against detailed manual analysis of the same wavefronts with conventional isochronal mapping. RETRO-Mapping was found to have comparable accuracy to isochronal mapping. RETRO-Mapping was then used to investigate atrial activation patterns during persistent AF. Atrial activation patterns demonstrated evidence of spatiotemporal stability over long time periods. Orbital plots created at different time points in the same location remained unchanged. Together with this important discovery, both fractionation and bipolar voltage were also demonstrated to express stability over time. Spatiotemporal stability during persistent AF enables sequential mapping as an acceptable technique. This property also allowed the development of a method for displaying sequentially mapped locations on a single map – RETRO-Choropleth Map. These findings go against the multiple wavelet hypothesis with random activation. Having gained insights in to these stable activation patterns, extensive analysis was undertaken to identify the presence of focal activation. Focal activations were identified during persistent AF. RETRO-Mapping was used to show that adjacent activation patterns were not related to focal activations. Lastly, the effect of pulmonary vein isolation (PVI) was studied by mapping atrial activation patterns before and after PVI. RETRO-Mapping showed that PVI leads to increased organisation of AF in most patients, supporting a mechanistic role of the pulmonary veins in persistent AF. In conclusion, a new technique has been developed and validated for automated activation mapping of persistent AF. These techniques could be used to guide additional ablation strategies beyond PVI for patients with persistent AF.Open Acces

Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

The usefulness of nifekalant for activation mapping of premature beat-triggered atrial fibrillation: Suppression of atrial fibrillation initiation without inhibiting premature beat

AbstractA 66-year-old man underwent a second ablation for atrial fibrillation (AF). Intravenous isoproterenol administration caused the atrial premature beat (APB), triggering AF. The APB originated in the right atrium and invariably initiated AF. Therefore, contact activation mapping could not be performed without frequent electrocardioversion. To prevent the initiation of AF without inhibiting the APB firing, we administered nifekalant intravenously, which facilitated precise activation mapping and ablation of the AF-triggering APB. The administration of nifekalant may improve clinical outcomes of catheter ablation for AF triggered by non-pulmonary vein APB, which invariably initiates AF

Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification

Image classification is a challenging problem which aims to identify the category of object in the image. In recent years, deep Convolutional Neural Networks (CNNs) have been applied to handle this task, and impressive improvement has been achieved. However, some research showed the output of CNNs can be easily altered by adding relatively small perturbations to the input image, such as modifying few pixels. Recently, Capsule Networks (CapsNets) are proposed, which can help eliminating this limitation. Experiments on MNIST dataset revealed that capsules can better characterize the features of object than CNNs. But it's hard to find a suitable quantitative method to compare the generalization ability of CNNs and CapsNets. In this paper, we propose a new image classification task called Top-2 classification to evaluate the generalization ability of CNNs and CapsNets. The models are trained on single label image samples same as the traditional image classification task. But in the test stage, we randomly concatenate two test image samples which contain different labels, and then use the trained models to predict the top-2 labels on the unseen newly-created two label image samples. This task can provide us precise quantitative results to compare the generalization ability of CNNs and CapsNets. Back to the CapsNet, because it uses Full Connectivity (FC) mechanism among all capsules, it requires many parameters. To reduce the number of parameters, we introduce the Parameter-Sharing (PS) mechanism between capsules. Experiments on five widely used benchmark image datasets demonstrate the method significantly reduces the number of parameters, without losing the effectiveness of extracting features. Further, on the Top-2 classification task, the proposed PS CapsNets obtain impressive higher accuracy compared to the traditional CNNs and FC CapsNets by a large margin.Comment: This paper is under consideration at Computer Vision and Image Understandin