Fig. 1. Angularly multiplexed OPT system

Fig. 2. Reconstructed cell images. (a) XZ direction; (b) YZ direction

Fig. 3. Flow chart of zebrafish cell identification and tracking model

Fig. 4. Structure diagram of zebrafish cell recognition and tracking model

Fig. 5. Three-dimensional cell tracking framework

Fig. 6. Three-dimensional tracking algorithm flow chart

Fig. 7. Mask generated by Labelme. (a) Original map of cells; (b) mask map

Fig. 8. Data augmentation (a) Original; (b) distortion; (c) overturn; (d) add noise

Fig. 9. Comparison of loss and mAP under different learning rates. (a) Training loss under different learning rates; (b) validation loss under different learning rates; (c) mAP under different learning rates

Fig. 10. mAP change curves under different improvement losses

Fig. 11. Loss of 606 training sets when learning rate is 0.001

Fig. 12. Local comparison of segmentation results. (a) Original cell image; (b) watershed segmentation based on gradient transformation; (c) morphological segmentation; (d) segmentation based on U-Net; (e) segmentation based on Mask R-CNN; (f) segmentation based on Mask R-CNN++

Fig. 13. Effect of segmentation in data augmentation. (a) Distortion; (b) add noise; (c) overturn

Fig. 14. Results of tracking by DeepSort. (a) Part of cells in 20th frame; (b) part of cells in 30th frame

Fig. 15. Cell trajectory (a) Three-dimentional trajectory map of cells; (b) position of cell relative to Z axis of first frame changes