Framework for lung CT image segmentation based on UNet++
This addresses segmentation challenges for lung CT images, though it appears incremental as it builds on existing UNet++ methods.
The paper tackled overfitting and small dataset problems in lung CT image segmentation by proposing a UNet++-based framework with data augmentation, optimized neural network, and parameter fine-tuning modules, achieving 98.03% accuracy with reduced overfitting.
Recently, the state-of-art models for medical image segmentation is U-Net and their variants. These networks, though succeeding in deriving notable results, ignore the practical problem hanging over the medical segmentation field: overfitting and small dataset. The over-complicated deep neural networks unnecessarily extract meaningless information, and a majority of them are not suitable for lung slice CT image segmentation task. To overcome the two limitations, we proposed a new whole-process network merging advanced UNet++ model. The network comprises three main modules: data augmentation, optimized neural network, parameter fine-tuning. By incorporating diverse methods, the training results demonstrate a significant advantage over similar works, achieving leading accuracy of 98.03% with the lowest overfitting. potential. Our network is remarkable as one of the first to target on lung slice CT images.