文献类型：期刊文献

英文题名：WGAN-based multi-structure segmentation of vertebral cross-section MRI using ResU-Net and clustered transformer

作者：Liu, Jing[1];Suo, Guodong[1];Jin, Fengqing[1];Zhou, Yuee[1];Yang, Jianlan[2]

第一作者：刘婧;刘静;刘晶

通信作者：Yang, JL[1]

机构：[1]Gansu Univ Chinese Med, Sch Med Informat Engn, Lanzhou 730000, Gansu, Peoples R China;[2]Orthoped Traumatol Hosp, Quanzhou 362019, Fujian, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Orthoped Traumatol Hosp, Quanzhou 362019, Fujian, Peoples R China.

年份：2024

卷号：14

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：;Scopus(收录号：2-s2.0-85209481509);WOS:【SCI-EXPANDED(收录号:WOS:001352833400001)】；

基金：This research was supported by the following project: Quanzhou Science and Technology Plan Project (CQZ2023N002S0004).

语种：英文

外文关键词：Residual U-Net; Clustered transformer; Lumbar spine MRI; WGAN; Multi-structure

摘要：The vertebral foramen, lamina, and vertebral body are three critical components of the spine structure, essential for maintaining spinal connectivity and stability. Accurately segmenting lumbar structures such as the vertebral body, vertebral foramen, and lamina in MRI cross-sections helps doctors better understand and diagnose the pathological causes of spine-related diseases. This study presents a multi-structure semantic segmentation method for vertebral transverse section MRI slices using WGAN with a residual U-Net and clustered Transformer. The generator network was replaced with a combination of a residual U-Net and a clustered Transformer-based segmentation network. The enhanced U-Net encoder, utilizing dilated convolutions and residual structures, improved multi-scale feature extraction capabilities. Meanwhile, the clustered Transformer structure, with reduced progressive linear complexity, ensured the extraction of global positional information. The results of multiple experiments show that the Dice coefficient for vertebral body segmentation increased by 3.1%, the Hausdorff distance decreased by 0.6 mm, mIOU improved by 4.1-96.2%, and PPV increased by 2.0-98.8% compared to mainstream segmentation models. These improvements are statistically significant (p < 0.05).Ablation experiments further validated the effectiveness of the proposed enhanced modules in improving segmentation accuracy for the three target structures.