IAUNet：实例感知U-Net

摘要

实例分割在生物医学成像中至关重要，它能精确区分如细胞等常重叠且大小各异的单个对象。近期，基于查询的方法（即通过对象查询引导分割）已展现出卓越性能。尽管U-Net一直是医学图像分割的首选架构，其在基于查询方法中的潜力却尚未充分挖掘。本研究中，我们提出了IAUNet，一种创新的基于查询的U-Net架构。其核心设计采用完整的U-Net架构，并辅以新型轻量级卷积像素解码器，从而提升模型效率并减少参数数量。此外，我们提出了一种Transformer解码器，用于在多尺度上精炼对象特定特征。最后，我们发布了2025年Revvity全细胞分割数据集，这一独特资源包含明场图像中重叠细胞质的详细标注，为生物医学实例分割设立了新基准。在多个公开数据集及我们自有数据上的实验表明，IAUNet在多数全卷积、基于Transformer及查询的模型以及专门针对细胞分割的模型中表现优异，为细胞实例分割任务奠定了坚实基础。代码已发布于https://github.com/SlavkoPrytula/IAUNet。

English

Instance segmentation is critical in biomedical imaging to accurately distinguish individual objects like cells, which often overlap and vary in size. Recent query-based methods, where object queries guide segmentation, have shown strong performance. While U-Net has been a go-to architecture in medical image segmentation, its potential in query-based approaches remains largely unexplored. In this work, we present IAUNet, a novel query-based U-Net architecture. The core design features a full U-Net architecture, enhanced by a novel lightweight convolutional Pixel decoder, making the model more efficient and reducing the number of parameters. Additionally, we propose a Transformer decoder that refines object-specific features across multiple scales. Finally, we introduce the 2025 Revvity Full Cell Segmentation Dataset, a unique resource with detailed annotations of overlapping cell cytoplasm in brightfield images, setting a new benchmark for biomedical instance segmentation. Experiments on multiple public datasets and our own show that IAUNet outperforms most state-of-the-art fully convolutional, transformer-based, and query-based models and cell segmentation-specific models, setting a strong baseline for cell instance segmentation tasks. Code is available at https://github.com/SlavkoPrytula/IAUNet

IAUNet：实例感知U-Net

IAUNet: Instance-Aware U-Net

摘要

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