MCV-UNet: a modified convolution & transformer hybrid encoder-decoder network with multi-scale information fusion for ultrasound image semantic segmentation.
PeerJ Comput Sci
; 10: e2146, 2024.
Article
en En
| MEDLINE
| ID: mdl-38983210
ABSTRACT
In recent years, the growing importance of accurate semantic segmentation in ultrasound images has led to numerous advances in deep learning-based techniques. In this article, we introduce a novel hybrid network that synergistically combines convolutional neural networks (CNN) and Vision Transformers (ViT) for ultrasound image semantic segmentation. Our primary contribution is the incorporation of multi-scale CNN in both the encoder and decoder stages, enhancing feature learning capabilities across multiple scales. Further, the bottleneck of the network leverages the ViT to capture long-range high-dimension spatial dependencies, a critical factor often overlooked in conventional CNN-based approaches. We conducted extensive experiments using a public benchmark ultrasound nerve segmentation dataset. Our proposed method was benchmarked against 17 existing baseline methods, and the results underscored its superiority, as it outperformed all competing methods including a 4.6% improvement of Dice compared against TransUNet, 13.0% improvement of Dice against Attention UNet, 10.5% improvement of precision compared against UNet. This research offers significant potential for real-world applications in medical imaging, demonstrating the power of blending CNN and ViT in a unified framework.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
PeerJ Comput Sci
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos