Application of TransUnet Deep Learning Model for Automatic Segmentation of Cervical Cancer in Small-Field T2WI Images.

Effective segmentation of cervical cancer tissue from magnetic resonance (MR) images is crucial for automatic detection, staging, and treatment planning of cervical cancer. This study develops an innovative deep learning model to enhance the automatic segmentation of cervical cancer lesions. We obtained 4063 T2WI small-field sagittal, coronal, and oblique axial images from 222 patients with pathologically confirmed cervical cancer.