Difference between revisions of "Publications/zhao.19.stacom"
From LRDE
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 volume = 12009 
 volume = 12009 

 pages = 405 to 413 
 pages = 405 to 413 

−   abstract = Automatic segmentation of the left ventricle (LV) of a living human heart in a magnetic resonance (MR) image (2D+t) allows to measure some clinical significant indices like the regional wall thicknesses (RWT), cavity dimensions, cavity and myocardium areas, and cardiac phase. Here, we propose a novel framework made of a sequence of two fully convolutional networks (FCN). The first is a modified temporallike VGG16 (the "localization network") and is used to localize roughly the LV (filledin) epicardium position in each MR volume. The second FCN is a modified temporallike VGG16 too, but devoted to segment the LV myocardium and cavity (the "segmentation network"). We evaluate the proposed method with 5foldcrossvalidation on the MICCAI 2019 LV Full Quantification Challenge dataset. For the network used to localize the epicardium, we obtain an average dice index of 0.8953 on validation set. For the segmentation network, we obtain an average dice index of 0.8664 on validation set (there, data augmentation is used). The mean absolute error (MAE) of average cavity and myocardium areas, dimensions, RWT are 
+   abstract = Automatic segmentation of the left ventricle (LV) of a living human heart in a magnetic resonance (MR) image (2D+t) allows to measure some clinical significant indices like the regional wall thicknesses (RWT), cavity dimensions, cavity and myocardium areas, and cardiac phase. Here, we propose a novel framework made of a sequence of two fully convolutional networks (FCN). The first is a modified temporallike VGG16 (the "localization network") and is used to localize roughly the LV (filledin) epicardium position in each MR volume. The second FCN is a modified temporallike VGG16 too, but devoted to segment the LV myocardium and cavity (the "segmentation network"). We evaluate the proposed method with 5foldcrossvalidation on the MICCAI 2019 LV Full Quantification Challenge dataset. For the network used to localize the epicardium, we obtain an average dice index of 0.8953 on validation set. For the segmentation network, we obtain an average dice index of 0.8664 on validation set (there, data augmentation is used). The mean absolute error (MAE) of average cavity and myocardium areas, dimensions, RWT are 114.77~mm^2; 0.9220~mm; 0.9185~mm respectively. The computation time of the pipeline is less than 2~s for an entire 3D volume. The error rate of phase classification is 7.6364%, which indicates that the proposed approach has a promising performance to estimate all these parameters. 
 lrdepaper = http://www.lrde.epita.fr/dload/papers/zhao.19.stacom.pdf 
 lrdepaper = http://www.lrde.epita.fr/dload/papers/zhao.19.stacom.pdf 

 lrdeprojects = Olena 
 lrdeprojects = Olena 

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(there, data augmentation is used). The mean absolute error 
(there, data augmentation is used). The mean absolute error 

(MAE) of average cavity and myocardium areas, dimensions, 
(MAE) of average cavity and myocardium areas, dimensions, 

−  RWT are 
+  RWT are 114.77~mm^2; 0.9220~mm; 0.9185~mm respectively. The 
−  +  computation time of the pipeline is less than 2~s for an 

−  +  entire 3D volume. The error rate of phase classification is 

−  +  7.6364\%, which indicates that the proposed approach has a 

−  +  promising performance to estimate all these parameters.<nowiki>}</nowiki> 

−  all these parameters.<nowiki>}</nowiki> 

<nowiki>}</nowiki> 
<nowiki>}</nowiki> 

Revision as of 17:19, 19 February 2020
 Authors
 Zhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud
 Where
 Statistical Atlases and Computational Models of the Heart. MultiSequence CMR Segmentation, CRTEPiggy and LV Full Quantification Challenges—10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers
 Type
 inproceedings
 Publisher
 Springer
 Projects
 Olena
 Keywords
 Image
 Date
 20200207
Abstract
Automatic segmentation of the left ventricle (LV) of a living human heart in a magnetic resonance (MR) image (2D+t) allows to measure some clinical significant indices like the regional wall thicknesses (RWT), cavity dimensions, cavity and myocardium areas, and cardiac phase. Here, we propose a novel framework made of a sequence of two fully convolutional networks (FCN). The first is a modified temporallike VGG16 (the "localization network") and is used to localize roughly the LV (filledin) epicardium position in each MR volume. The second FCN is a modified temporallike VGG16 too, but devoted to segment the LV myocardium and cavity (the "segmentation network"). We evaluate the proposed method with 5foldcrossvalidation on the MICCAI 2019 LV Full Quantification Challenge dataset. For the network used to localize the epicardium, we obtain an average dice index of 0.8953 on validation set. For the segmentation network, we obtain an average dice index of 0.8664 on validation set (there, data augmentation is used). The mean absolute error (MAE) of average cavity and myocardium areas, dimensions, RWT are 114.77~mm^2; 0.9220~mm; 0.9185~mm respectively. The computation time of the pipeline is less than 2~s for an entire 3D volume. The error rate of phase classification is 7.6364%, which indicates that the proposed approach has a promising performance to estimate all these parameters.
Documents
Bibtex (lrde.bib)
@InProceedings{ zhao.19.stacom, author = {Zhou Zhao and Nicolas Boutry and \'Elodie Puybareau and Thierry G\'eraud}, title = {A TwoStage TemporalLike Fully Convolutional Network Framework for Left Ventricle Segmentation and Quantification on {MR} Images}, booktitle = {Statistical Atlases and Computational Models of the Heart. MultiSequence CMR Segmentation, CRTEPiggy and LV Full Quantification Challenges10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers}, year = 2020, editor = {Mihaela Pop and Maxime Sermesant and Oscar Camara and Xiahai Zhuang and Shuo Li and Alistair Young and Tommaso Mansi and Avan Suinesiaputra}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, volume = {12009}, pages = {405413}, abstract = {Automatic segmentation of the left ventricle (LV) of a living human heart in a magnetic resonance (MR) image (2D+t) allows to measure some clinical significant indices like the regional wall thicknesses (RWT), cavity dimensions, cavity and myocardium areas, and cardiac phase. Here, we propose a novel framework made of a sequence of two fully convolutional networks (FCN). The first is a modified temporallike VGG16 (the "localization network") and is used to localize roughly the LV (filledin) epicardium position in each MR volume. The second FCN is a modified temporallike VGG16 too, but devoted to segment the LV myocardium and cavity (the "segmentation network"). We evaluate the proposed method with 5foldcrossvalidation on the MICCAI 2019 LV Full Quantification Challenge dataset. For the network used to localize the epicardium, we obtain an average dice index of 0.8953 on validation set. For the segmentation network, we obtain an average dice index of 0.8664 on validation set (there, data augmentation is used). The mean absolute error (MAE) of average cavity and myocardium areas, dimensions, RWT are 114.77~mm^2; 0.9220~mm; 0.9185~mm respectively. The computation time of the pipeline is less than 2~s for an entire 3D volume. The error rate of phase classification is 7.6364\%, which indicates that the proposed approach has a promising performance to estimate all these parameters.} }