A Brief Study on Automated Non Contrast Cardiac MRI Segmentation by Machine Vision Techniques

Raman, Valliappan; Then, Patrick; Rapaee, Annuar

doi:10.7763/ijet.2012.v4.420

Cited by 2 publications

(1 citation statement)

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“…The segmentation of the heart requires the identification of two anatomical regions: the inner part called the endocardium, and the outer part, the epicardium. Identifying and segmenting those two regions in CMR images presents different levels of difficulty: while the endocardium has sufficient contrast, the epicardial surface presents profiles of intensity with little contrast [3].…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation on the Entire Cardiac Cycle Using a Deep Learning Work - Flow

Savioli¹,

Vieira

Lamata

et al. 2018

2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS)

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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.

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Section: Introductionmentioning

confidence: 99%