Microscopy data is often large and 3d, and thus convolutional neural networks (CNNs) need to be applied in a tile-and-stitch manner to cope with GPU memory constraints. Concerning pixel-wise predictions obtained with UNet-style CNNs via tile-and-stitch, issues with discontinuities at output tile boundaries have been reported. However, a formal analysis of the causes has been lacking. In...
Recent developments in bio-imaging technologies have allowed researchers to collect larger and larger tomographic datasets which contain an immense amount of details. To achieve a quantitative understanding, however, these datasets need to be cleaned-up and segmented. These two tasks are tedious, very time consuming, and still performed mostly manually. In our work we aim to develop a full...
L12-type nano-ordered structures are typically fully-coherent with FCC matrix, which is challengeable to be characterized. Spatial distribution maps are used to probe local order within reconstructed APT data. However, it is almost impossible to manually analyse the complete point cloud in search for the partial crystallographic information retained within the data. Here, we proposed an...
Characterizing crystallographic interfaces in synthetic nanomaterials is an important step for the design of novel materials. Trained materials scientists can assign interface structures of materials by looking at high-resolution imaging and diffraction data obtained by aberration-corrected scanning transmission electron microscopy (STEM). However, STEM datasets cannot be fully exploited due...
