Adversarial Image Alignment and Interpolation

TL;DR

This paper introduces a neural network-based method using adversarial training for image synthesis, alignment, and super-resolution interpolation of 3D biomedical images, improving reconstruction quality.

Contribution

It presents a novel adversarial neural network framework for image synthesis, alignment, and super-resolution interpolation in volumetric biomedical imaging.

Findings

Effective synthesis of missing or damaged data from adjacent sections.

Precise fine-scale alignment of electron microscopy data.

Generation of isotropic volumes from anisotropic images.

Abstract

Volumetric (3d) images are acquired for many scientific and biomedical purposes using imaging methods such as serial section microscopy, CT scans, and MRI. A frequent step in the analysis and reconstruction of such data is the alignment and registration of images that were acquired in succession along a spatial or temporal dimension. For example, in serial section electron microscopy, individual 2d sections are imaged via electron microscopy and then must be aligned to one another in order to produce a coherent 3d volume. State of the art approaches find image correspondences derived from patch matching and invariant feature detectors, and then solve optimization problems that rigidly or elastically deform series of images into an aligned volume. Here we show how fully convolutional neural networks trained with an adversarial loss function can be used for two tasks: (1) synthesis of missing or damaged image data from adjacent sections, and (2) fine-scale alignment of block-face electron microscopy data. Finally, we show how these two capabilities can be combined in order to produce artificial isotropic volumes from anisotropic image volumes using a super-resolution adversarial alignment and interpolation approach.