IAUNet: Instance-Aware U-Net

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.

Revvity-25. One of our key contributions in this paper is a novel cell instance segmentation dataset named Revvity-25. It includes 110 high-resolution 1080 x 1080 brightfield images, each containing, on average, 27 manually labeled and expert-validated cancer cells, totaling 2937 annotated cells. To our knowledge, this is the first dataset with accurate and detailed annotations for cell borders and overlaps, with each cell annotated using an average of 60 polygon points, reaching up to 400 points for more complex structures. Revvity-25 dataset provides a unique resource that opens new possibilities for testing and benchmarking models for modal and amodal semantic and instance segmentation.