首页|CT-UFormer: an improved hybrid decoder for image segmentation
CT-UFormer: an improved hybrid decoder for image segmentation
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NETL
NSTL
Springer Nature
Abstract Segmentation of lung nodules in medical images is crucial for early detection and treatment planning of lung cancer. The nnU-Net has achieved significant success in numerous medical image segmentation tasks due to its efficient design. However, nnU-Net demonstrates limitations in capturing long-range dependencies. In contrast, the Transformer model effectively manages long-range dependencies through global self-attention mechanisms. In this paper, we propose an improved nnU-Net with a Transformer decoder to segment lung nodules and identify regions of interest. The data augmentation module can increase the amount of available data. It includes two key components: 1) GAN generates lung nodules to increase the number of available datasets; 2) hybrid decoder captures multi-scale feature maps to enable the refinement of “organ label sets.” Our extensive evaluations on two datasets of different sizes, MSD-Lung and LIDC-IDRI, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. Our results are superior to commonly used state-of-the-art works. Compared to the results of existing improved Transformers, our method performs excellently in terms of DSC, MASD, and HD95 metrics. This study proposes a new lung nodule segmentation method, which has higher accuracy and robustness compared to commonly used methods. The method automatically performs effective data augmentation on input data and balances global features and local details through a hybrid decoder. Code is available at https://github.com/andou6/CT-UFormer.
NETL
NSTL
Springer Nature
