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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. ① Select particles from micrographs. The coordinates can come from manual or semi-manual particle selection software. ② Perform reconstruction using mainstream software, such as RELION and EMAN. Record the fine-tuned Euler angles and translation parameters. ③ Generate corresponding re-projection images for each particle. ④ Adjust the coordinates based on the translation parameters. ⑤ 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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