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

2009 ◽  
Author(s):  
Marie-pierre Jolly

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.

2017 ◽  
Vol 56 (6) ◽  
pp. 1053-1062 ◽  
Author(s):  
Li Kuo Tan ◽  
Yih Miin Liew ◽  
Einly Lim ◽  
Yang Faridah Abdul Aziz ◽  
Kok Han Chee ◽  
...  

2008 ◽  
Vol 59 (4) ◽  
pp. 771-778 ◽  
Author(s):  
Peter Kellman ◽  
Christophe Chefd'hotel ◽  
Christine H. Lorenz ◽  
Christine Mancini ◽  
Andrew E. Arai ◽  
...  

2006 ◽  
Vol 23 (5) ◽  
pp. 641-651 ◽  
Author(s):  
Amol S. Pednekar ◽  
Raja Muthupillai ◽  
Veronica V. Lenge ◽  
Ioannis A. Kakadiaris ◽  
Scott D. Flamm

2021 ◽  
Vol 19 (2) ◽  
pp. 1591-1608
Author(s):  
Ke Bi ◽  
◽  
Yue Tan ◽  
Ke Cheng ◽  
Qingfang Chen ◽  
...  

<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>


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