Cervical‐YOSA: Utilizing prompt engineering and pre‐trained large‐scale models for automated segmentation of multi‐sequence MRI images in cervical cancer

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.

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Publication Details

Journal
IET Image Processing
Published
2024-09-05
DOI
https://doi.org/10.1049/ipr2.13194
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Cervical‐YOSA: Utilizing prompt engineering and pre‐trained large‐scale models for automated segmentation of multi‐sequence MRI images in cervical cancer

Yanfen Cui, Zhengjie Ou, Yanwei Xia, Qiang Liu et al.
1 links
IET Image Processing
Cervical Cancer and HPV Research
article

Cervical‐YOSA: Utilizing prompt engineering and pre‐trained large‐scale models for automated segmentation of multi‐sequence MRI images in cervical cancer

Yanfen Cui, Zhengjie Ou, Yanwei Xia, Qiang Liu, Da Teng, Dan Zhao, Lihua Tan
article en

Abstract

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.

IET Image ProcessingVol. 18(12)
Beijing Institute of Petrochemical Technology (CN), Shanxi Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Beijing Academy of Artificial Intelligence (CN)
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
Partnerships for the goals
Openalex Percentile: Top 77%
Cervical Cancer and HPV Research
DOIOpen Access PDFOpenAlex
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