Abstract
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.
Original language | English |
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Title of host publication | 2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 153-158 |
Number of pages | 6 |
ISBN (Electronic) | 9781538695883 |
DOIs | |
Publication status | Published - 30 Nov 2018 |
Event | 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018 - Valencia, Spain Duration: 15 Oct 2018 → 18 Oct 2018 |
Conference
Conference | 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018 |
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Country/Territory | Spain |
City | Valencia |
Period | 15/10/2018 → 18/10/2018 |