Segmentation of three-dimensional (3D) electron microscopy (EM) image stacks is an arduous and tedious task. Deep convolutional neural networks (CNNs) work well to automate the segmentation; however, they require a large training dataset, which is a major impediment. In order to solve this issue, especially for sparse segmentation, we used a CNN with a minimal training dataset.
View Article and Find Full Text PDFFluorescence microscopy is used extensively in cell-biological and biomedical research, but it is often plagued by three major problems with the presently available fluorescent probes: photobleaching, blinking, and large size. We have addressed these problems, with special attention to single-molecule imaging, by developing biocompatible, red-emitting silicon nanocrystals (SiNCs) with a 4.1-nm hydrodynamic diameter.
View Article and Find Full Text PDF