Affiliation:
1. Academy of Artificial Intelligence Beijing Institute of Petrochemical Technology Beijing China
2. Department of Gynecology Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
3. Department of Radiology Shanxi Province Cancer Hospital Shanxi Medical University Taiyuan China
Abstract
AbstractCervical cancer is a major health concern, particularly in developing countries with limited medical resources. This study introduces two models aimed at improving cervical tumor segmentation: a semi‐automatic model that fine‐tunes the Segment Anything Model (SAM) and a fully automated model designed for efficiency. Evaluations were conducted using a dataset of 8586 magnetic resonance imaging (MRI) slices, where the semi‐automatic model achieved a Dice Similarity Coefficient (DSC) of 0.9097, demonstrating high accuracy. The fully automated model also performed robustly with a DSC of 0.8526, outperforming existing methods. These models offer significant potential to enhance cervical cancer diagnosis and treatment, especially in resource‐limited settings.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Qinghai Province
Beijing Municipal Education Commission
Publisher
Institution of Engineering and Technology (IET)