  As a result of the rise of Transformer architectures in medical image
analysis, specifically in the domain of medical image segmentation, a multitude
of hybrid models have been created that merge the advantages of Convolutional
Neural Networks (CNNs) and Transformers. These hybrid models have achieved
notable success by significantly improving segmentation accuracy. Yet, this
progress often comes at the cost of increased model complexity, both in terms
of parameters and computational demand. Moreover, many of these models fail to
consider the crucial interplay between spatial and channel features, which
could further refine and improve segmentation outcomes. To address this, we
introduce LHU-Net, a Light Hybrid U-Net architecture optimized for volumetric
medical image segmentation. LHU-Net is meticulously designed to prioritize
spatial feature analysis in its initial layers before shifting focus to
channel-based features in its deeper layers, ensuring a comprehensive feature
extraction process. Rigorous evaluation across five benchmark datasets -
Synapse, LA, Pancreas, ACDC, and BRaTS 2018 - underscores LHU-Net's superior
performance, showcasing its dual capacity for efficiency and accuracy. Notably,
LHU-Net sets new performance benchmarks, such as attaining a Dice score of
92.66 on the ACDC dataset, while simultaneously reducing parameters by 85% and
quartering the computational load compared to existing state-of-the-art models.
Achieved without any reliance on pre-training, additional data, or model
ensemble, LHU-Net's effectiveness is further evidenced by its state-of-the-art
performance across all evaluated datasets, utilizing fewer than 11 million
parameters. This achievement highlights that balancing computational efficiency
with high accuracy in medical image segmentation is feasible. Our
implementation of LHU-Net is freely accessible to the research community on
GitHub.
