用自监督学习让模型更懂细胞图像，性能提升超10%。
Self-supervised Pretraining of Cell Segmentation Models
- 基于DINOv2在无标注细胞图像上继续自监督训练
- LIVECell上分割得分0.784，比现有方法高10.42%
- 零样本迁移能力强，适合跨数据集的细胞分析
实例分割可通过识别显微图像中每个细胞的像素，分析细胞的空间与时间特性。然而，高质量标注的显微图像数据集稀缺，限制了进展。近期许多方法通过使用大规模自然图像模型（如SAM）的预训练权重初始化模型来缓解此问题。但自然图像中学习到的物体性与纹理先验与显微图像不匹配，导致域偏移下性能下降。本文提出DINOCell，一种自监督框架，利用DINOv2的表征，并在未标注细胞图像上进行持续自监督训练，再进行有监督微调。在LIVECell基准上，DINOCell取得0.784的SEG分数，较领先的SAM基线提升10.42%，并在三个分布外显微图像数据集上展现出强零样本性能。结果表明，领域适配的自监督预训练对鲁棒细胞分割至关重要。
原文摘要 · Abstract (English)
Instance segmentation enables the analysis of spatial and temporal properties of cells in microscopy images by identifying the pixels belonging to each cell. However, progress is constrained by the scarcity of high-quality labeled microscopy datasets. Many recent approaches address this challenge by initializing models with segmentation-pretrained weights from large-scale natural-image models such as Segment Anything Model (SAM). However, representations learned from natural images often encode objectness and texture priors that are poorly aligned with microscopy data, leading to degraded performance under domain shift. We propose DINOCell, a self-supervised framework for cell instance segmentation that leverages representations from DINOv2 and adapts them to microscopy through continued self-supervised training on unlabeled cell images prior to supervised fine-tuning. On the LIVECell benchmark, DINOCell achieves a SEG score of 0.784, improving by 10.42% over leading SAM-based models, and demonstrates strong zero-shot performance on three out-of-distribution microscopy datasets. These results highlight the benefits of domain-adapted self-supervised pretraining for robust cell segmentation.
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