3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks

Curvilinear structures frequently appear in microscopy imaging as the object of interest. Crystallographic defects, i.edislocations, are one of the curvilinear structures that have been repeatedly investigated under transmission electronmicroscopy (TEM) and their 3D structural information is of great importance for understanding the properties ofmaterials. 3D information of dislocations is often obtained by tomography which is a cumbersome process since itis required to acquire many images with different tilt angles and similar imaging conditions. Although, alternativestereoscopy methods lower the number of required images to two, they still require human intervention and shape priorsfor accurate 3D estimation. We propose a fully automated pipeline for both detection and matching of curvilinearstructures in stereo pairs by utilizing deep convolutional neural networks (CNNs) without making any prior assumptionon 3D shapes. In this work, we mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.