提出新评估指标MMA，解决细胞分割评分不稳不准问题
Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching
- 用全局最优一对一匹配替代传统贪心匹配
- 在合成故障、渐进污染等测试中表现更稳定敏感
- 适合生物细胞图像分割模型的公平对比与评测
可靠的实例分割评估需准确反映分割质量。然而当前生物成像中广泛使用的评估指标存在根本性数学缺陷：硬性交并比阈值导致评分不连续且敏感度低；按对象归一化在物体大小变化时扭曲得分；贪心或一对多匹配产生非最优、依赖顺序的对应关系。这些特性在分裂、合并细胞及边界不精确等常见失败模式下导致评分不直观、不可靠。本文提出最大匹配准确率（MMA），一种无阈值的连续指标，通过全局最优的一对一匹配计算预测与真实对象间的总重叠，并采用像素级归一化。我们在三个实验中将MMA与AP@50、PQ、SEG和AJI进行对比：合成故障案例、渐进污染测试和模型排序比较。结果表明，MMA在稳定性、敏感性和可解释性上均优于现有方法，为生物细胞图像实例分割的基准评测提供了可靠基础。
原文摘要 · Abstract (English)
Reliable evaluation of instance segmentation models requires metrics that accurately and consistently reflect segmentation quality. However, the metrics most widely used in biological imaging carry fundamental mathematical weaknesses: hard Intersection-over-Union (IoU) thresholds that produce discontinuous, low sensitivity scoring; per-object normalization that distorts scores under object size variation; and greedy or one-to-many matching procedures that yield non-optimal, order-dependent correspondences. Together, these properties produce unintuitive and unreliable model rankings under common failure modes such as split cells, merged cells, and cell boundary imprecision. We propose Maximum Matching Accuracy (MMA), a threshold-free continuous metric that finds a globally optimal one-to-one matching between predicted and ground truth objects and aggregates total overlap using per-pixel normalization. We evaluate MMA against AP@50, PQ, SEG, and AJI across three experiments: synthetic failure cases, progressive corruption tests, and a model ranking comparison. MMA produces scores that are more stable, more sensitive, and more interpretable than existing alternatives, providing a principled foundation for fair instance segmentation benchmarking in biological cell imaging.
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