Fig. 1
From: PIXER: an automated particle-selection method based on segmentation using a deep neural network
The general workflow of the training and test processes of PIXER. The blue part of the image shows the training process for segmentation and classification network. The red part of the image shows the general flow of the test process. The test process works as follows: ①feed micrographs into the segmentation network; ② acquire probability density maps from the network; ③feed density maps to a selection algorithm; ④ generate the preliminary particle coordinates from probability density maps; ⑤ feed the preliminary results into the classification network; and ⑥ generate the results after removing false positive particles