Dense Deformation Network for High Resolution Tissue Cleared Image Registration
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The recent application of Deep Learning in various areas of medical image analysis has brought excellent performance gain. The application of deep learning technologies in medical image registration successfully outperformed traditional optimization based registration algorithms both in registration time and accuracy. In this paper, we present a densely connected convolutional architecture for deformable image registration. The training of the network is unsupervised and does not require ground-truth deformation or any synthetic deformation as a label. The proposed architecture is trained and tested on two different version of tissue cleared data, 10% and 25% resolution of high resolution dataset respectively and demonstrated comparable registration performance with the state-of-the-art ANTS registration method. The proposed method is also compared with the deep-learning based Voxelmorph registration method. Due to the memory limitation, original voxelmorph can work at most 15% resolution of Tissue cleared data. For rigorous experimental comparison we developed a patch-based version of Voxelmorph network, and trained it on 10% and 25% resolution. In both resolution, proposed DenseDeformation network outperformed Voxelmorph in registration accuracy.
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