The accuracy and robustness of segmentation models are critical considerations in the processing of mouse brain electron microscopy images. We proposed a highly robust two-dimensional segmentation algorithm tailored to the technical characteristics of electron microscopy images， aiming to accurately delineate the morphological structure of cells in each slice. Aiming at accurately delineate the morphological structure of cells in each section，a highly robust two-dimensional segmentation algorithm based on natural image model tailored to the technical characteristics of electron microscopy images was proposed. EM-SAM was based on fine-tuning the backbone network of pre-trained large natural image model SAM for maximizing the capability of features extraction. The model employed the image encoder from the SAM architecture， augmented with a U-shaped decoder， and was fine-tuned specifically for the segmentation of mouse brain electron microscopy images. Experimental results demonstrate that A-Rand achieves 0.054 on public dataset SNEMI3D. Additionally， AP-50 and AP-75 reach 0.883 and 0.604， respectively， on public dataset MitoEM. EM-SAM exhibits high accuracy and robustness in neural segmentation tasks of electron microscopy images， and it can be fine-tuned for different tasks.