IAUNet: Instance-Aware U-Net

• URL: http://arxiv.org/abs/2508.01928v1
• Date: Sun, 03 Aug 2025 21:36:20 GMT
• Title: IAUNet: Instance-Aware U-Net
• Authors: Yaroslav Prytula, Illia Tsiporenko, Ali Zeynalli, Dmytro Fishman,
• Abstract summary: IAUNet is a novel query-based U-Net architecture for instance segmentation.<n>We show that IAUNet outperforms most state-of-the-art fully convolutional, transformer-based, and query-based models and cell segmentation-specific models.
• Score: 1.9249287163937978
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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