IAUNet: Instance-Aware U-Net

TL;DR

IAUNet introduces a novel query-based U-Net architecture with a lightweight decoder and Transformer refinement, achieving superior performance in biomedical cell instance segmentation and establishing a new benchmark dataset.

Contribution

The paper presents IAUNet, a new query-based U-Net model with a lightweight decoder and Transformer components, enhancing efficiency and accuracy in biomedical instance segmentation.

Findings

Outperforms state-of-the-art models on multiple datasets

Reduces model parameters with a lightweight decoder

Sets a new benchmark with the 2025 Revvity Full Cell Segmentation Dataset

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