Paper Info
Title
A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MR Images
Abstract
In this work, we describe an unsupervised deep learning framework featuring a Laplacian-based operator as smoothing loss for deformable registration of 3D cine cardiac magnetic resonance (CMR) images. Before registration, the input 3D images are corrected for slice misalignment by segmenting the left ventricle (LV) blood-pool, LV myocardium and right ventricle (RV) blood-pool using a U-Net model and aligning the 2D slices along the center of the LV blood-pool. We conducted experiments using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We used the registration deformation field to warp the manually segmented LV blood-pool, LV myocardium and RV blood-pool labels from end-diastole (ED) frame to the other frames in the cardiac cycle. We achieved a mean Dice score of 94.84%, 85.22% and 84.36%, and Hausdorff distance (HD) of 2.74 mm, 5.88 mm and 9.04 mm, for the LV blood-pool, LV myocardium and RV blood-pool, respectively. We also introduce a pipeline to estimate patient tractography using the proposed CNN-based cardiac motion estimation.
Year
DOI
Venue
2020
10.22489/CinC.2020.204
2020 Computing in Cardiology
Keywords
DocType
Volume
convolutional neural network-based deformable image registration method,cine cardiac MR images,unsupervised deep learning framework,Laplacian-based operator,smoothing loss,deformable registration,3D cine cardiac magnetic resonance images,input 3D images,slice misalignment,left ventricle blood-pool,LV blood-pool,Automated Cardiac Diagnosis Challenge dataset,registration deformation field,cardiac cycle,CNN-based cardiac motion estimation
Conference
47
ISSN
ISBN
Citations 
2325-8861
978-1-7281-1105-6
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Roshan Reddy Upendra100.34
Brian Jamison Wentz200.34
Suzanne M. Shontz300.34
Cristian A. Linte400.34