{"ID":2887437,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01928","arxiv_id":"2508.01928","title":"IAUNet: Instance-Aware U-Net","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","short_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 segmentati...","url_abs":"https://arxiv.org/abs/2508.01928","url_pdf":"https://arxiv.org/pdf/2508.01928v1","authors":"[\"Yaroslav Prytula\",\"Illia Tsiporenko\",\"Ali Zeynalli\",\"Dmytro Fishman\"]","published":"2025-08-03T21:36:20Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":611434,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2887437,"paper_url":"https://arxiv.org/abs/2508.01928","paper_title":"IAUNet: Instance-Aware U-Net","repo_url":"https://github.com/SlavkoPrytula/IAUNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}