无需标注数据，通过置信度引导实现细胞分割精度突破。
COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
- 用最优传输增强细小细胞的检测敏感度，提升无标注分割初始效果。
- 基于预测与修正掩码的一致性生成实例置信度，替代真实标注。
- 通过递归自蒸馏逐步扩大高置信度实例，显著提升分割性能。
细胞实例分割在组织病理图像中对识别单个细胞形态至关重要，为生物医学研究提供关键信息。尽管无监督细胞实例分割（UCIS）旨在减少对耗时标注的依赖，但其难以精确捕捉细胞边界，导致漏检和性能不佳。针对缺乏无错误实例这一核心问题，本文提出COIN（置信度引导的实例蒸馏）框架，包含三个关键步骤：(1) 通过最优传输的无监督语义分割提高对微小实例的敏感度；(2) 建立实例级置信度评分，衡量模型预测与精修掩码的一致性，以识别高置信度实例，替代真实标注；(3) 通过递归自蒸馏逐步扩展高置信度实例。在六个数据集上的实验表明，COIN超越现有无监督方法，在MoNuSeg和TNBC数据集上甚至优于半监督与弱监督方法，所有指标均领先。代码已开源。
原文摘要 · Abstract (English)
Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detections and poor performance. Recognizing the absence of error-free instances as a key limitation, we present COIN (COnfidence score-guided INstance distillation), a novel annotation-free framework with three key steps: (1) Increasing the sensitivity for the presence of error-free instances via unsupervised semantic segmentation with optimal transport, leveraging its ability to discriminate spatially minor instances, (2) Instance-level confidence scoring to measure the consistency between model prediction and refined mask and identify highly confident instances, offering an alternative to ground truth annotations, and (3) Progressive expansion of confidence with recursive self-distillation. Extensive experiments across six datasets show COIN outperforming existing UCIS methods, even surpassing semi- and weakly-supervised approaches across all metrics on the MoNuSeg and TNBC datasets. The code is available at https://github.com/shjo-april/COIN.
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