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. Code is available at
https://github.com/SlavkoPrytula/IAUNet
6891b03ef01a094725f8360e
Yaroslav Prytula
claimed_verified
2025-08-06T19:20:46.531Z
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Yaroslav Prytula
user
YaroslavPrytula
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Illia Tsiporenko
claimed_verified
2025-08-08T16:23:06.098Z
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Illia Tsiporenko
user
its561
6891b03ef01a094725f83610
Ali Zeynalli
claimed_verified
2025-08-14T13:40:22.796Z
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Ali Zeynalli
user
alizeyn
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Dmytro Fishman
claimed_verified
2025-08-08T16:23:09.565Z
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Dmytro Fishman
user
skyfallen
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. Code is available at
https://github.com/SlavkoPrytula/IAUNet
IAUNet, a query-based U-Net architecture with a lightweight convolutional Pixel decoder and Transformer decoder, outperforms state-of-the-art models in biomedical instance segmentation.
U-Net Pixel decoder Transformer decoder query-based methods biomedical instance segmentation Revvity Full Cell Segmentation Dataset