Instance segmentation of mouse brain scanning electron microscopy images based on fine-tuning nature image model
The accuracy and robustness of segmentation models are critical considerations in the process-ing of mouse brain electron microscopy images.We proposed a highly robust two-dimensional segmenta-tion 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 morphologi-cal structure of cells in each section,a highly robust two-dimensional segmentation algorithm based on natu-ral 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.
万方数据
维普
