Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses

Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses

Abstract Objective To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian-adnexal reporting and data system (O-RADS). Methods...

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Bibliographic Details
Main Authors: Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:Insights into Imaging
Subjects:
Ultrasound
Segmentation
Machine learning
Deep learning
Adnexal mass
Online Access:https://doi.org/10.1186/s13244-024-01874-7
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https://doi.org/10.1186/s13244-024-01874-7

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