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.
IEEE Trans Biomed Eng 63(8), 1563–1572 (2016)
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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.