English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The segmentation of the left ventricle (LV) from CINE MRI images is essentialto infer important clinical parameters. Typically, machine learning algorithmsfor automated LV segmentation use annotated contours from only two cardiacphases, diastole, and systole. In this work, we present an analysis work-flowfor fully-automated LV segmentation that learns from images acquired throughthe cardiac cycle. The workflow consists of three components: first, for eachimage in the sequence, we perform an automated localization and subsequentcropping of the bounding box containing the cardiac silhouette. Second, weidentify the LV contours using a Temporal Fully Convolutional Neural Network(T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through arecurrent 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 potentialsand Semantic Flow. Our initial experiments suggest that significant improvementin performance can potentially be achieved by using a recurrent neural networkcomponent that explicitly learns cardiac motion patterns whilst performing LVsegmentation.

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