Framework for lung CT image segmentation based on UNet++

2025/01/05 by Hao Ziang, Ziang, Hao, Jingsi Zhang +3 · 1 citation
Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2501.02428

openalex publication_date 2025/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning

Related

• UNet Based Pipeline for Lung Segmentation from Chest X-Ray Images
• CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans
• U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
• Automatic Airway Segmentation in chest CT using Convolutional Neural Networks
• UNet++: A Nested U-Net Architecture for Medical Image Segmentation