Left Atrial Segmentation Combining Multi-atlas Whole Heart Labeling and Shape-Based Atlas Selection

Segmentation of the left atria (LA) from late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is challenging since atrial borders are not easily distinguishable in the images. We propose a method based on multi-atlas whole heart segmentation and shape modeling of the LA. In the training phase we first construct whole heart LGE-MRI atlases and build a principal component analysis (PCA) model able to capture the high variability of the LA shapes. All atlases are clustered according to their LA shape using an unsupervised clustering method which additionally outputs the most representative case in each cluster. All cluster representatives are registered to the target image and ranked using conditional entropy. A small subset of the most similar representatives is used to find LA shapes with similar morphology in the training set that are used to obtain the final LA segmentation. We tested our approach using 80 LGE-MRI data for training and 20 LGE-MRI data for testing obtaining a Dice score of \(0.842 \pm 0.049\).

[1]  R. J. van der Geest,et al.  Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium‐enhanced MRI: Towards objective atrial scar assessment , 2016, Journal of magnetic resonance imaging : JMRI.

[2]  Jürgen Weese,et al.  Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets , 2015, IEEE Transactions on Medical Imaging.

[3]  Daniel Rueckert,et al.  Non-rigid registration of cardiac MR: application to motion modelling and atlas-based segmentation , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[4]  Xiahai Zhuang,et al.  Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI , 2016, Medical Image Anal..

[5]  Sébastien Ourselin,et al.  A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI , 2010, IEEE Transactions on Medical Imaging.

[6]  Guang Yang,et al.  Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation , 2018, MICCAI.

[7]  Stanley Nattel,et al.  The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. , 2014, Circulation research.

[8]  Josien P W Pluim,et al.  Multiatlas-based segmentation with preregistration atlas selection. , 2013, Medical physics.

[9]  Marta Nuñez-Garcia,et al.  Sensitivity analysis of geometrical parameters to study haemodynamics and thrombus formation in the left atrial appendage , 2018, International journal for numerical methods in biomedical engineering.

[10]  K. Bhat,et al.  Variations in the pulmonary venous ostium in the left atrium and its clinical importance. , 2014, Journal of clinical and diagnostic research : JCDR.

[11]  Ashutosh Kumar Singh,et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, Lancet.

[12]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[13]  Dan J Stein,et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2015, The Lancet.

[14]  Mert R. Sabuncu,et al.  Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..

[15]  Mert R. Sabuncu,et al.  Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation , 2010, STACOM/CESC.

[16]  Yaozong Gao,et al.  Learning-Based Atlas Selection for Multiple-Atlas Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Daniel Rueckert,et al.  Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. , 2015, Medical physics.