提出新模型QCell，解决显微镜下细胞重叠分割难题
QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation
- 用查询重组与对比对齐机制，在隐空间拆解并重构重叠细胞
- 在ISBI2014上提升2.2 AP和2.7 AJI，优于现有方法
- 适用于复杂组织结构的细胞实例分割任务
显微镜下重叠细胞的实例分割因半透明结构导致边界弱、重叠区域视觉信息混合而困难。现有方法依赖局部区域或形状先验，缺乏跨重叠对象的全局推理能力。我们提出QCell，一种新型基于查询的模型，用于显微镜场景中细胞实例的去重叠。该方法包含：(i) 实例重组模块，将查询表示在隐空间中分解与重组，使模型能在重叠条件下推理完整物体结构；(ii) 对比查询对齐目标，结合显著性实例特征学习与重叠细胞查询分离。我们还引入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在多个基准上超越现有最先进方法，在ISBI2014上实现+2.2 AP和+2.7 AJI的提升。代码已公开于https://github.com/SlavkoPrytula/QCell。
原文摘要 · Abstract (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
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