Table 1.

Conditions for training different segmentation models.

Model	Backbone	Pre-Proc.	Augmentation
Model 1	Efficient0	N/A	DA
Model 2	Efficient0	HE	DA
Model 3	Efficient0	HE	DA + Gaussian noise (0.5) + gamma correction (0.5) + grid distortion (0.1) + elastic transform (0.1) + affine transform (0.1)
Model 4	Efficient7	HE	DA + Gaussian noise (0.5) + gamma correction (0.5)
Model 5	Efficient7	HE	DA + Gaussian noise (0.5) + gamma correction (0.5) + grid distortion (0.1) + elastic transform (0.1) + affine transform (0.1)

Abbreviations: HE, histogram equalization; DA, default augmentation (horizontal flip: 0.5, rotation: a range of ±25°, random contrast: 0.1, random brightness 0.1, gamma correction: 0.1, Gaussian noise: 0.1, contrast limited adaptive histogram equalization 0.1).