Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation

The EffiCell-Seg framework has been introduced to enhance cell segmentation in computational pathology by efficiently leveraging Vision Foundation Models (VFMs) without the need to retrain heavy visual encoders. This approach utilizes a Cell Structure Prompt Encoder (CSP-Encoder) to extract structural priors from frozen VFM representations and a Synergistic Mask Decoder (SM-Decoder) for contextual consistency, achieving superior performance with only ~5M trainable parameters—over 130 times fewer than conventional fine-tuning methods. This framework significantly reduces computational overhead and annotation dependency, making it a valuable tool for practitioners in biomedical imaging.