Echo4DIR reconstructs continuous 4D cardiac geometry from sparse 2D echocardiography videos using implicit representations, epipolar feature fusion, self-supervised domain adaptation, and radial SDF alignment to achieve up to 98.35% Dice overlap.
In: Proceedings of the IEEE interna- tional conference on computer vision
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
Cycle-consistent CNN enables unsupervised 3D deformable registration of medical images, shown on liver CT for more accurate cancer size estimation.
citing papers explorer
-
Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videos
Echo4DIR reconstructs continuous 4D cardiac geometry from sparse 2D echocardiography videos using implicit representations, epipolar feature fusion, self-supervised domain adaptation, and radial SDF alignment to achieve up to 98.35% Dice overlap.
-
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
Cycle-consistent CNN enables unsupervised 3D deformable registration of medical images, shown on liver CT for more accurate cancer size estimation.