Faculty Opinions recommendation of Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

2019 ◽  
Author(s):  
Stamatios Lerakis
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Short Axis ◽  
Cine Mr ◽  
Cardiac Cine ◽  
Ventricle Segmentation ◽  
Mr Sequences

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.

Multi-Scale Feature Fusion Convolutional Neural Network for Concurrent Segmentation of Left Ventricle and Myocardium in Cardiac MR Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1023-1032
Author(s):  
Lin Qi ◽  
Haoran Zhang ◽  
Xuehao Cao ◽  
Xuyang Lyu ◽  
Lisheng Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Left Ventricular ◽  
Mr Images ◽  
Feature Maps ◽  
Scale Feature ◽  
Multi Scale ◽  
Cine Mr

Accurate segmentation of the blood pool of left ventricle (LV) and myocardium (or left ventricular epicardium, MYO) from cardiac magnetic resonance (MR) can help doctors to quantify LV ejection fraction and myocardial deformation. To reduce doctor’s burden of manual segmentation, in this study, we propose an automated and concurrent segmentation method of the LV and MYO. First, we employ a convolutional neural network (CNN) architecture to extract the region of interest (ROI) from short-axis cardiac cine MR images as a preprocessing step. Next, we present a multi-scale feature fusion (MSFF) CNN with a new weighted Dice index (WDI) loss function to get the concurrent segmentation of the LV and MYO. We use MSFF modules with three scales to extract different features, and then concatenate feature maps by the short and long skip connections in the encoder and decoder path to capture more complete context information and geometry structure for better segmentation. Finally, we compare the proposed method with Fully Convolutional Networks (FCN) and U-Net on the combined cardiac datasets from MICCAI 2009 and ACDC 2017. Experimental results demonstrate that the proposed method could perform effectively on LV and MYOs segmentation in the combined datasets, indicating its potential for clinical application.

Automatic localization of the left ventricular blood pool centroid in short axis cardiac cine MR images

2017 ◽  
Vol 56 (6) ◽  
pp. 1053-1062 ◽  
Author(s):  
Li Kuo Tan ◽  
Yih Miin Liew ◽  
Einly Lim ◽  
Yang Faridah Abdul Aziz ◽  
Kok Han Chee ◽  
...  
Keyword(s):  
Blood Pool ◽  
Left Ventricular ◽  
Short Axis ◽  
Mr Images ◽  
Cine Mr ◽  
Cardiac Cine ◽  
Automatic Localization

Left ventricle segmentation for cine MR using deep learning

2021 ◽  
pp. 37-57
Author(s):  
Hisham Abdeltawab ◽  
Fahmi Khalifa ◽  
Fatma Taher ◽  
Mohammed Ghazal ◽  
Ali H. Mahmoud ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Cine Mr ◽  
Ventricle Segmentation
Sign in / Sign up

Export Citation Format

Share Document