提出新型查询式U-Net，提升生物医学图像中重叠细胞分割精度
IAUNet: Instance-Aware U-Net
- 基于U-Net架构，融合轻量级像素解码器与多尺度Transformer解码器
- 在多个公开数据集上优于主流全卷积、Transformer及查询式模型
- 配套发布2025年Revvity全细胞分割数据集，推动细胞实例分割研究
实例分割在生物医学成像中至关重要，用于精确区分重叠且大小不一的细胞等对象。近期基于查询的方法（通过对象查询引导分割）表现优异。尽管U-Net是医学图像分割的常用架构，其在基于查询方法中的潜力尚未被充分探索。本文提出IAUNet，一种新颖的基于查询的U-Net架构。核心设计采用完整U-Net结构，并引入轻量级卷积像素解码器，提升效率并减少参数量。此外，提出Transformer解码器，在多尺度上优化对象特定特征。最后，发布2025 Revvity全细胞分割数据集，包含明场图像中重叠细胞胞质的精细标注，为生物医学实例分割设立新基准。在多个公开数据集及自建数据上的实验表明，IAUNet显著优于多数前沿全卷积、Transformer及查询式模型，以及专用于细胞分割的模型，为细胞实例分割任务建立强基线。代码已开源。
原文摘要 · Abstract (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
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