A Three-Step Automated Segmentation Method for Early Cervical Cancer MRI Images Based on Deep Learning

Abstract

Tumor detection and segmentation are essential for cervical cancer (CC) treatment and diagnosis. This study presents a model that segmented the tumor, uterus, and vagina based on deep learning automatically on magnetic resonance imaging (MRI) images of patients with CC. The tumor detection dataset consists of 68 CC patients' diffusion-weighted magnetic resonance imaging (DWI) images. The segmented dataset consists of 73 CC patients' T2-weighted imaging (T2WI) images. First, the three clear images of the patient's DWI images are detected using a single-shot multibox detector (SSD). Second, the serial number of the clearest image is obtained by scores, while the corresponding T2WI image with the same serial number is selected. Third, the selected images are segmented by employing the semantic segmentation (U-Net) model with the squeeze-and-excitation (SE) block and attention gate (SE-ATT-Unet). Three segmentation models are implemented to automatically segment the tumor, uterus, and vagina separately by adding different attention mechanisms at different locations. The target detection accuracy of the model is 92.32%, and the selection accuracy is 90.9%. The dice similarity coefficient (DSC) on the tumor is 92.20%, pixel accuracy (PA) is 93.08%, and the mean Hausdorff distance (HD) is 3.41 mm. The DSC on the uterus is 93.63%, PA is 91.75%, and the mean HD is 9.79 mm. The DSC on the vagina is 75.70%, PA is 85.46%, and the mean HD is 10.52 mm. The results show that the proposed method accurately selects images for segmentation, and the SE-ATT-Unet is effective in segmenting different regions on MRI images.