Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional LSTM

2018 ◽  
Author(s):  
Antong Chen ◽  
Dongqing Zhang ◽  
Ilknur Icke ◽  
Belma Dogdas ◽  
Sarayu Parimal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Left Ventricle ◽  
Convolutional Neural Networks ◽  
Mr Images ◽  
Fully Convolutional Neural Networks ◽  
Cine Mr ◽  
Cardiac Cine ◽  
Convolutional Lstm ◽  
Left Ventricle Myocardium

scholarly journals Evaluation of the Dynamic Deformable Elastic Template model for the segmentation of the heart in MRI sequences

2009 ◽  
Author(s):  
Christopher Casta ◽  
Patrick Clarysse ◽  
Joël Schaerer ◽  
Jérome Pousin
Keyword(s):  
Left Ventricle ◽  
Cine Mri ◽  
Mr Images ◽  
Temporal Segmentation ◽  
Cine Mr ◽  
Current Implementation ◽  
Segmentation Accuracy ◽  
Mri Sequences ◽  
Spatio Temporal ◽  
Left Ventricle Myocardium

We introduce a bio-inspired dynamic deformable (DET) model based on the equation of dynamics and including temporal smoothness constraints. The behaviour and characteristics of the dynamic DET model is studied in the context of the semi automatic spatio-temporal segmentation of the left ventricle myocardium in cine-MR images. The segmentation accuracy for endo/epicardium contours at end-diastole and end-systole, and as consequence the performance and limits of the current implementation, is evaluated in the context of the MICCAI LV Segmentation Challenge on a database of 15 multi-slice cine-MRI examinations.

scholarly journals Automatic identification of the left ventricle in cardiac cine-MR images: Dual-contrast cluster analysis and scout-geometry approaches

2006 ◽  
Vol 23 (5) ◽  
pp. 641-651 ◽  
Author(s):  
Amol S. Pednekar ◽  
Raja Muthupillai ◽  
Veronica V. Lenge ◽  
Ioannis A. Kakadiaris ◽  
Scott D. Flamm
Keyword(s):  
Cluster Analysis ◽  
Left Ventricle ◽  
Automatic Identification ◽  
Mr Images ◽  
Cine Mr ◽  
Cardiac Cine

Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks

2017 ◽  
Author(s):  
Liset Vázquez Romaguera ◽  
Marly Guimarães Fernandes Costa ◽  
Francisco Perdigón Romero ◽  
Cicero Ferreira Fernandes Costa Filho
Keyword(s):  
Neural Networks ◽  
Left Ventricle ◽  
Convolutional Neural Networks ◽  
Cardiac Mri ◽  
Fully Convolutional Neural Networks ◽  
Ventricle Segmentation

scholarly journals Sequential shape similarity for active contour based left ventricle segmentation in cardiac cine MR image

2021 ◽  
Vol 19 (2) ◽  
pp. 1591-1608
Author(s):  
Ke Bi ◽  
◽  
Yue Tan ◽  
Ke Cheng ◽  
Qingfang Chen ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Active Contour ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Shape Similarity ◽  
The Novel ◽  
Mr Images ◽  
Cine Mr ◽  
Cardiac Cine ◽  
The Mean

<abstract> <p>Delineation of the boundaries of the Left Ventricle (LV) in cardiac Magnetic Resonance Images (MRI) is a hot topic due to its important diagnostic power. In this paper, an approach is proposed to extract the LV in a sequence of MR images. In the proposed paper, all images in the sequence are segmented simultaneously and the shape of the LV in each image is supposed to be similar to that of the LV in nearby images in the sequence. We coined the novel shape similarity constraint, and it is called sequential shape similarity (SSS in short). The proposed segmentation method takes the Active Contour Model as the base model and our previously proposed Gradient Vector Convolution (GVC) external force is also adopted. With the SSS constraint, the snake contour can accurately delineate the LV boundaries. We evaluate our method on two cardiac MRI datasets and the Mean Absolute Distance (MAD) metric and the Hausdorff Distance (HD) metric demonstrate that the proposed approach has good performance on segmenting the boundaries of the LV.</p> </abstract>

scholarly journals Fully Automatic Left Ventricle Segmentation in Cardiac Cine MR Images Using Registration and Minimum Surfaces

2009 ◽  
Author(s):  
Marie-pierre Jolly
Keyword(s):  
Left Ventricle ◽  
Shortest Paths ◽  
Blood Pool ◽  
Grand Challenge ◽  
Mr Images ◽  
Cine Mr ◽  
Cardiac Cine ◽  
Cine Study ◽  
Fully Automatic ◽  
Ventricle Segmentation

This paper describes a fully automatic system to segment the left ventricle in all slices and all phases of a magnetic resonance cardiac cine study. After localizing the left ventricle blood pool using motion, thresholding and clustering, slices are segmented sequentially. For each slice, deformable registration is used to align all the phases, candidate contours are recovered in the average image using shortest paths, and a minimal surface is built to generate the final contours. The advantage of our method is that the resulting contours follow the edges in each phase and are consistent over time. As part of the MICCAI grand challenge on left ventricle segmentation, we demonstrate using 15 training datasets and 15 validation datasets that the results are very good with average errors around 2 mm and the method is ready for clinical routine.

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