Cervical‐YOSA: Utilizing prompt engineering and pre‐trained large‐scale models for automated segmentation of multi‐sequence MRI images in cervical cancer
Year: 2024
Type: article
Abstract: Cervical 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.
Source: IET Image Processing
DOI: 10.1049/ipr2.13194
Authors: Yanwei Xia, Zhengjie Ou, Lihua Tan, Qiang Liu, Yanfen Cui, Da Teng, Dan Zhao
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,610
Funders: National Natural Science Foundation of China, Beijing Municipal Education Commission, Chinese Academy of Medical Sciences, Shanxi Medical University, Peking Union Medical College, Cancer Foundation of China, Beijing Institute of Petrochemical Technology, Natural Science Foundation of Qinghai
OpenAlex ID: W4402322824