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Fig. 2 | BMC Bioinformatics

Fig. 2

From: PIXER: an automated particle-selection method based on segmentation using a deep neural network

Fig. 2

Illustrations of the PIXER methods. (a) The architecture of the classification and segmentation networks. (b) Workflow of generating training data for segmentation. Select particles from micrographs. The coordinates can come from manual or semi-manual particle selection software. Perform reconstruction using mainstream software, such as RELION and EMAN. Record the fine-tuned Euler angles and translation parameters. Generate corresponding re-projection images for each particle. Adjust the coordinates based on the translation parameters. Fit these re-projection images back into the label image of each micrograph. (c) Procedure for the grid-based, local-maximum particle-selection method. Step 1: Generate the maximum value for each grid. Steps 2 and 3: Perform a parallel local-maximum searching method to locate local-maximum values during the iteration. Step 4: Select the local-maximum results

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