Probabilistic dense displacement network for weakly-supervised medical image registration uses approximate min-convolutions and mean field inference to achieve state-of-the-art accuracy on inter-patient abdominal CT, outperforming prior deep learning by 15% Dice overlap.
NeuroImage 158, 378–396 (2017)
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Cycle-consistent CNN enables unsupervised 3D deformable registration of medical images, shown on liver CT for more accurate cancer size estimation.
Two training mechanisms for unsupervised deep registration networks reduce the number of locations with negative Jacobian determinants in predicted deformations.
citing papers explorer
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Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks
Probabilistic dense displacement network for weakly-supervised medical image registration uses approximate min-convolutions and mean field inference to achieve state-of-the-art accuracy on inter-patient abdominal CT, outperforming prior deep learning by 15% Dice overlap.
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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.
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On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks
Two training mechanisms for unsupervised deep registration networks reduce the number of locations with negative Jacobian determinants in predicted deformations.