Automated segmentation on the entire cardiac cycle using a deep learning work-flow

The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-automated LV segmentation that learns from images acquired through the cardiac cycle. The workflow consists of three components: first, for each image in the sequence, we perform an automated localization and subsequent cropping of the bounding box containing the cardiac silhouette. Second, we identify the LV contours using a Temporal Fully Convolutional Neural Network (T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a recurrent mechanism enforcing temporal coherence across consecutive frames. Finally, we further defined the boundaries using either one of two components: fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials and Semantic Flow. Our initial experiments suggest that significant improvement in performance can potentially be achieved by using a recurrent neural network component that explicitly learns cardiac motion patterns whilst performing LV segmentation.Comment: 6 pages, 2 figures, published on IEEE Xplor

A Low Complexity Scheme for Passive UWB-RFID: Proof of Concept

International audiencePassive UWB-RFID technology represents an emerging solution capable of guaranteeing extremely low energy consumption and high-accuracy localization at the same time. One of the most critical tasks is the acquisition of the tag code at reader side, which can be complex, time-and resource-consuming when multiple UWB tags are deployed. This letter proposes a simple and effective approach, based on a specific assignment strategy of the tag code, which drastically simplifies code acquisition by guaranteeing high tag detection performance. A real system implementation adopting this strategy is shown to prove its feasibility in terms of real-time multiple tags detection and localization