Framework for lung CT image segmentation based on UNet++

TL;DR

This paper introduces a specialized UNet++ based framework for lung CT image segmentation that effectively addresses overfitting and small dataset challenges, achieving high accuracy and robustness.

Contribution

It presents a novel whole-process network combining data augmentation, an optimized UNet++ model, and parameter fine-tuning tailored for lung CT segmentation.

Findings

Achieved 98.03% accuracy in lung CT segmentation

Significantly reduced overfitting compared to existing models

Demonstrated effectiveness on small datasets

Abstract

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.