scholarly journals Segmentation of the Left Ventricle from Cine MR Images Using a Comprehensive Approach

2009 ◽  
Author(s):  
Su Huang ◽  
Jimin Liu ◽  
Looi Chow Lee ◽  
Sudhakar K Venkatesh ◽  
Lynette Li San Teo ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Gold Standard ◽  
Practical Method ◽  
Comprehensive Approach ◽  
Mr Images ◽  
Multiple Image ◽  
Cardiac Image ◽  
Cine Mr ◽  
Segmentation Methods ◽  
Cardiac Image Segmentation

Segmentation of the left ventricle is important in assessment of cardiac functional parameters. Currently, manual segmentation is the gold standard for acquiring these parameters and can be time-consuming. Therefore, accuracy and automation are two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach that utilizes various features of cine MR images and combines multiple image processing methods including thresholding, edge detection, mathematical morphology, deformable model as well as image filtering. The segmentation is performed automatically with minimized interaction to optimize segmentation results. This approach provides cardiac radiologists a practical method for accurate segmentation of the left ventricle.

scholarly journals An Image-Based Comprehensive Approach for Automatic Segmentation of Left Ventricle from Cardiac Short Axis Cine MR Images

2010 ◽  
Vol 24 (4) ◽  
pp. 598-608 ◽  
Author(s):  
Su Huang ◽  
Jimin Liu ◽  
Looi Chow Lee ◽  
Sudhakar K Venkatesh ◽  
Lynette Li San Teo ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Automatic Segmentation ◽  
Comprehensive Approach ◽  
Short Axis ◽  
Short Axis Cine ◽  
Mr Images ◽  
Cine Mr

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.

RA-SIFA: Unsupervised domain adaptation multi-modality cardiac segmentation network combining parallel attention module and residual attention unit

2021 ◽  
pp. 1-14
Author(s):  
Tiejun Yang ◽  
Xiaojuan Cui ◽  
Xinhao Bai ◽  
Lei Li ◽  
Yuehong Gong
Keyword(s):  
Image Segmentation ◽  
Domain Adaptation ◽  
Mr Images ◽  
Unsupervised Domain Adaptation ◽  
Cardiac Image ◽  
Cardiac Segmentation ◽  
Study Results ◽  
Whole Heart ◽  
Cardiac Image Segmentation ◽  
Feature Alignment

BACKGROUND: Convolutional neural network has achieved a profound effect on cardiac image segmentation. The diversity of medical imaging equipment brings the challenge of domain shift for cardiac image segmentation. OBJECTIVE: In order to solve the domain shift existed in multi-modality cardiac image segmentation, this study aims to investigate and test an unsupervised domain adaptation network RA-SIFA, which combines a parallel attention module (PAM) and residual attention unit (RAU). METHODS: First, the PAM is introduced in the generator of RA-SIFA to fuse global information, which can reduce the domain shift from the respect of image alignment. Second, the shared encoder adopts the RAU, which has residual block based on the spatial attention module to alleviate the problem that the convolution layer is insensitive to spatial position. Therefore, RAU enables to further reduce the domain shift from the respect of feature alignment. RA-SIFA model can realize the unsupervised domain adaption (UDA) through combining the image and feature alignment, and then solve the domain shift of cardiac image segmentation in a complementary manner. RESULTS: The model is evaluated using MM-WHS2017 datasets. Compared with SIFA, the Dice of our new RA-SIFA network is improved by 8.4%and 3.2%in CT and MR images, respectively, while, the average symmetric surface distance (ASD) is reduced by 3.4 and 0.8mm in CT and MR images, respectively. CONCLUSION: The study results demonstrate that our new RA-SIFA network can effectively improve the accuracy of whole-heart segmentation from CT and MR images.

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

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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