Dataset Information

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

ABSTRACT: Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters.

Supplementary information

The online version contains supplementary material available at 10.1007/s11760-022-02302-3.

SUBMITTER: Rajamani KT

PROVIDER: S-EPMC9311338 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

Rajamani Kumar T KT Rani Priya P Siebert Hanna H ElagiriRamalingam Rajkumar R Heinrich Mattias P MP

Signal, image and video processing 20220725 4

Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model ...[more]

PMID: 35910403

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Dataset Information

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

Supplementary information

Publications

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

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