Adapting Segment Anything Model (SAM) through Prompt-based Learning for
  Enhanced Protein Identification in Cryo-EM Micrographs
Fei He Zhiyuan Yang Mingyue Gao Biplab Poudel Newgin Sam Ebin Sam Dhas Rajan Gyawali Ashwin Dhakal Jianlin Cheng Dong Xu
Cryo-electron microscopy (cryo-EM) remains pivotal in structural biology, yet
the task of protein particle picking, integral for 3D protein structure
construction, is laden with manual inefficiencies. While recent AI tools such
as Topaz and crYOLO are advancing the field, they do not fully address the
challenges of cryo-EM images, including low contrast, complex shapes, and
heterogeneous conformations. This study explored prompt-based learning to adapt
the state-of-the-art image segmentation foundation model Segment Anything Model
(SAM) for cryo-EM. This focus was driven by the desire to optimize model
performance with a small number of labeled data without altering pre-trained
parameters, aiming for a balance between adaptability and foundational
knowledge retention. Through trials with three prompt-based learning
strategies, namely head prompt, prefix prompt, and encoder prompt, we observed
enhanced performance and reduced computational requirements compared to the
fine-tuning approach. This work not only highlights the potential of prompting
SAM in protein identification from cryo-EM micrographs but also suggests its
broader promise in biomedical image segmentation and object detection.