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Updated: Jun 4, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Not Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with
Lei Cao1, Qikai Zhang1, Chunjiang Fan2
1Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, China.
Background:
Medical image segmentation is crucial for clinical diagnostics and treatment planning. Recently, hybrid models often neglect the local modeling capabilities of Transformers for medical image segmentation, despite the complementary nature of local information from both convolutional neural networks (CNNs) and transformers. This limitation is particularly problematic in multi-organ segmentation, where organs are closely adhered, and accurate delineation is essential. This study aims to develop a novel method that leverages the strengths of both CNNs and transformers to address the challenges of segmenting multiple closely adhered organs, improving accuracy and robustness in multi-organ segmentation.
Methods:
In this study, we present the Not Another Dual Attention block (NNDA-block), a versatile, plug-and-play module that seamlessly integrates channel and spatial attention mechanisms. This block can be incorporated at any stage within the Not Another Dual Attention UNet Transformer (NNDA-UNETR) framework. Our novel approach to spatial attention uniquely combines local modeling, significantly reducing detail and texture loss during the up- and down-sampling processes typical in U-shaped architectures.
Results:
Evaluations on the Beyond the Cranial Vault (BTCV) and Automatic Cardiac Diagnosis Challenge benchmark datasets demonstrate our method's effectiveness in balancing model complexity with accuracy. On the BTCV dataset, our model sets new state-of-the-art benchmarks, achieving a Dice similarity coefficient of 84.13%, normalized surface dice of 86.96%, mean average surface distance of 3.46 mm, and an Hausdorff distance at 95% of 17.76 mm. Moreover, it reduces the parameter count by 34.2% compared to leading contemporary methods, all while maintaining low computational costs [measured in floating point operations per second (FLOPs)].
Conclusions:
NNDA-UNETR offers a robust solution for accurate segmentation in multi-organ tasks, particularly where organ adhesion poses challenges. Its lightweight design also makes it well-suited for deployment in real-world medical environments with limited computational resources.
