ArticleComputing in cardiology2020
A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MR Images.
Article in Computing in cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.Magnetic resonance in medicine · 2026Article
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- Deformable Image Registration Using Vision Transformers for Cardiac Motion Estimation from Cine Cardiac MRI Images.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2023Article
- Motion Extraction of the Right Ventricle from 4D Cardiac Cine MRI Using A Deep Learning-Based Deformable Registration Framework.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.