QCell：重组与对齐细胞查询用于重叠实例分割

摘要

显微镜中重叠细胞的实例分割仍具挑战性，原因在于半透明结构产生弱边界，且重叠区域呈现混合视觉证据。现有方法通过局部感兴趣区域或形状先验来处理该问题，但缺乏跨越重叠对象的全局推理能力。我们提出QCell，一种新颖的基于查询的模型，用于解重叠显微镜场景中的细胞实例。我们的方法结合了：(i) 实例重组模块，在潜在空间中分解并重组查询表示，使模型能够在重叠情况下推理完整的对象结构；以及(ii) 对比查询对齐目标，该目标结合了判别性实例特征学习与重叠细胞查询的分离。我们还引入了一个新的类器官数据集基准，用于重叠细胞分割。实验表明，QCell在多个基准上优于现有最先进方法，在ISBI2014上实现了+2.2 AP和+2.7 AJI的提升。代码可在 https://github.com/SlavkoPrytula/QCell 获取。

English

Instance segmentation of overlapping cells in microscopy remains challenging due to semi-transparent structures that produce weak boundaries and mixed visual evidence in overlap regions. Existing methods address this through local regions of interest or shape priors but lack global reasoning across overlapping objects. We present QCell, a novel query-based model that de-overlaps cell instances in microscopy scenes. Our approach combines (i) an instance recombination module that decomposes and recombines query representations in latent space, enabling the model to reason about complete object structure under overlap, and (ii) a contrastive query alignment objective that combines distinctive instance feature learning and separation of overlapping cell queries. We additionally introduce a new Organoid dataset benchmark for overlapping cell segmentation. We show that QCell outperforms state-of-the-art methods across multiple benchmarks, achieving +2.2 AP and +2.7 AJI on ISBI2014. Code is available at https://github.com/SlavkoPrytula/QCell