Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging
Fig 5
Impacts on prediction accuracy from smaller training sets.
(A) Cropping a sample image into 64 sub-images. (B) A comparison of network prediction accuracy as a function of training set size. The U-Net is trained with the complete dataset as described in S1 Table for each experimental condition. Then, random images representing a fraction of the total training set is used to train a new U-Net from scratch. The average number of cells per sub-image were: MDCK 5x: 107 cells per sub-image; Keratinocyte 10x: 26 cells per sub-image; MDCK 20x: 11 cells per sub-image; HUVEC 20x: 3 cells per sub-image. (C, D) display nuclei counts for representative images of MDCK cells (5x, 20x magnification, respectively) as a function of training set size, with each model independently trained. Practical readouts such as nuclei count may vary widely with small training set size. (E, F) display representative images for the HUVEC 20x dataset and the MDCK 20x dataset, respectively, with predictions shown for various training set sizes. All scale bars represent 30 μm.