Study on microwave ablation temperature prediction model based on grayscale ultrasound texture and machine learning.

ABSTRACT:

Background

Temperature prediction is crucial in the clinical ablation treatment of liver cancer, as it can be used to estimate the coagulation zone of microwave ablation.

Methods

Experiments were conducted on 83 fresh ex vivo porcine liver tissues at two ablation powers of 15 W and 20 W. Ultrasound grayscale images and temperature data from multiple sampling points were collected. The machine learning method of random forests was used to train the selected texture features, obtaining temperature prediction models for sampling points and the entire ultrasound imaging area. The accuracy of the algorithm was assessed by measuring the area of the hyperechoic area in the porcine liver tissue cross-section and ultrasound grayscale images.

Results

The model exhibited a high

SUBMITTER: Xiong Y 

PROVIDER: S-EPMC11423965 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature