IAUNet：實例感知U-Net

摘要

實例分割在生物醫學影像中至關重要，它能精確區分如細胞等常重疊且大小不一的個體。近期基於查詢的方法，即通過物件查詢引導分割，已展現出卓越性能。儘管U-Net在醫學影像分割中一直是首選架構，其在基於查詢方法中的潛力卻尚未充分挖掘。本研究提出IAUNet，一種新穎的基於查詢的U-Net架構。其核心設計特色在於完整的U-Net架構，並通過創新的輕量級卷積像素解碼器增強，使模型更為高效並減少參數量。此外，我們提出了一種Transformer解碼器，用於在多尺度上精煉物件特徵。最後，我們引入了2025年Revvity全細胞分割數據集，這是一份獨特的資源，包含明場影像中重疊細胞質的詳細標註，為生物醫學實例分割設立了新基準。在多個公開數據集及我們自有的數據集上的實驗表明，IAUNet在大多數最先進的全卷積、基於Transformer及基於查詢的模型以及專用於細胞分割的模型中表現優異，為細胞實例分割任務設立了堅實的基準。代碼可於https://github.com/SlavkoPrytula/IAUNet獲取。

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：實例感知U-Net

IAUNet: Instance-Aware U-Net

摘要

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