用可变形映射匹配细胞，自动标注密集显微图像。
DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images
- 将细胞标注转化为特征匹配与图像配准的结合任务。
- 仅需少量标注原型即可在大图中高效传播标签。
- 适用于任意像素级标注，适合生物医学图像分析者。
数字显微成像技术的快速发展带来了海量全片扫描图像，为生物医学研究和临床诊断提供了新机遇。然而，准确标注密集排列的细胞仍是重大挑战。为此，我们提出DiffKillR框架，将细胞标注重构为原型匹配与图像配准的联合任务。该框架包含两个互补的神经网络：一个学习对微分同胚不变的特征空间以实现鲁棒细胞匹配，另一个计算细胞间的精确扭曲场以完成标注映射。仅需少量标注原型，DiffKillR即可高效地将标注传播至大型显微图像，大幅减少人工标注需求。更重要的是，该方法适用于任意像素级标注任务。我们在三个显微图像任务上验证了其有效性，结果表明其优于现有的监督、半监督及无监督方法。代码已开源：https://github.com/KrishnaswamyLab/DiffKillR。
原文摘要 · Abstract (English)
The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods. The code is available at https://github.com/KrishnaswamyLab/DiffKillR.
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