# Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation

arXiv: https://arxiv.org/abs/2606.21913 · HTML: https://mlsanity.com/paper/2606.21913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation](https://mlsanity.com/paper/2102.06867/) (2021)
- [Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation](https://mlsanity.com/paper/2401.13220/) (2024)
- [Unleashing the Power of Prompt-driven Nucleus Instance Segmentation](https://mlsanity.com/paper/2311.15939/) (2023)
- [Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2508.20909/) (2025)
- [Segment Anything for Histopathology](https://mlsanity.com/paper/2502.00408/) (2025)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation](https://mlsanity.com/paper/2511.15771/) (2025)
- [Co-Seg++: Mutual Prompt-Guided Collaborative Learning for Versatile Medical Segmentation](https://mlsanity.com/paper/2506.17159/) (2025)
- [Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation](https://mlsanity.com/paper/2509.06740/) (2025)
- [FreqDINO: Frequency-Guided Adaptation for Generalized Boundary-Aware Ultrasound Image Segmentation](https://mlsanity.com/paper/2512.11335/) (2025)

## Abstract (arXiv)

Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remarkable universal feature representations, unlocking their full potential for cellular imaging is currently bottlenecked by resource-intensive adaptation paradigms. Existing methods typically rely on fine-tuning heavy visual encoders, leading to extensive computational overhead and a dependency on large-scale annotations. To address this, we propose the EffiCell-Seg framework for highly efficient cell segmentation without re-training the visual encoder. Our core insight is that pretrained VFMs intrinsically encode complementary structural priors: global saliency for localizing potential cells, and local morphological patterns for delineating cellular structures. To harness these priors, we devise a Cell Structure Prompt Encoder (CSP-Encoder) that synthesizes semantic-aware saliency and principal morphological features from frozen VFM representations into explicit structural prior maps. Moreover, we propose a Synergistic Mask Decoder (SM-Decoder) that enforces contextual consistency by jointly predicting geometric distance fields and semantic maps via mutual cross-guidance. Extensive experiments demonstrate that EffiCell-Seg outperforms state-of-the-art methods across diverse cell imaging modalities while requiring only ~5M trainable parameters, over 130x fewer than fully fine-tuned VFM counterparts. The code is available at https://github.com/xq141839/EffiCell-Seg.