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
@article{arxiv.2508.01928,
title = {IAUNet: Instance-Aware U-Net},
author = {Yaroslav Prytula and Illia Tsiporenko and Ali Zeynalli and Dmytro Fishman},
journal= {arXiv preprint arXiv:2508.01928},
year = {2025}
}
Comments
Published in CVPR Workshops (CVMI), 2025. Project page/code/models/dataset: $\href{https://slavkoprytula.github.io/IAUNet/}{\text{this https URL}}$