IAUNet: Instance-Aware U-Net
This work addresses the challenge of accurately segmenting individual, overlapping cells in biomedical images, which is incremental as it adapts query-based methods to the U-Net architecture.
The authors tackled the problem of instance segmentation in biomedical imaging, particularly for overlapping cells, by introducing IAUNet, a query-based U-Net architecture that outperforms state-of-the-art models on multiple datasets, including their newly created 2025 Revvity Full Cell Segmentation Dataset.
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