Application of improved Unet network in the recognition and segmentation of lung CT images in patients with pneumoconiosis

作者：Zhengsong Zhou, Xin Li, Hongbo Ji, Xuanhan Xu, Zongqi Chang, Keda Wu, Yangyang Song, Mingkun Kao, Hongjun Chen, Dongsheng Wu, Tao Zhang · 发表于：BMC Medical Imaging · 年份：2024 · DOI：10.1186/s12880-024-01377-3 · 被引用次数：10 · 研究领域：COVID-19 diagnosis using AI、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival. This study aims to evaluate the performance and application value of improved Unet network technology in the recognition and segmentation of lesion areas of lung CT images in patients with pneumoconiosis. METHODS: A total of 1212 lung CT images of patients with pneumoconiosis were retrospectively included. The improved Unet network was used to identify and segment the CT image regions of the patients' lungs, and the image data of the granular regions of the lungs were processed by the watershed and region growing algorithms. After random sorting, 848 data were selected into the training set and 364 data into the validation set. The experimental dataset underwent data augmentation and were used for model training and validation to evaluate segmentation performance. The segmentation results were compared with FCN-8s, Unet network (Base), Unet (Squeeze-and-Excitation, SE + Rectified Linear Unit, ReLU), and Unet + + networks. RESULTS: In the segmentation of lung CT granular region with the improved Unet network, the four evaluation indexes of Dice similarity coefficient, positive prediction value (PPV), sensitivity coefficient (SC) and mean intersection over union (MIoU) reached 0.848, 0.884, 0.895 and 0.885, respectively, increasing by 7.6%, 13.3%, 3.9% and 6.4%, respectively, compared with those of Unet network (Base), and increasing by 187.5%, 249.4%, 131.9% and 51.0%, respectively, compare...