Automatic Segmentation of Magnetic Resonance Images of Severe Patients with Advanced Liver Cancer and the Molecular Mechanism of Emodin-Induced Apoptosis of HepG2 Cells under the Deep Learning.
Journal:
Journal of healthcare engineering
PMID:
35295165
Abstract
To improve the accuracy of clinical diagnosis of severe patients with advanced liver cancer and enhance the effect of chemotherapy treatment, the U-Net model was optimized by introducing the batch normalization (BN) layer and the dropout layer, and the segmentation training and verification of the optimized model were realized by the magnetic resonance (MR) image data. Subsequently, HepG2 cells were taken as the research objects and treated with 0, 10, 20, 40, 60, 80, and 100 mol/L emodin (EMO), respectively. The methyl thiazolyl tetrazolium (MTT) method was used to explore the changes in cell viability, the acridine orange (AO)/ethidium bromide (EB) and 4',6-diamidino-2-phenylindole (DAPI) were used for staining, the Annexin V fluorescein isothiocyanate (FITC)/propidium iodide (PI) (Annexin V-FITC/PI) was adopted to detect the apoptosis after EMO treatment, and the Western blot (WB) method was used with the purpose of exploring the changes in protein expression levels of PARP, Bcl-2, and p53 in the cells after treatment. It was found that compared with the original U-Net model, the introduction of the BN layer and the dropout layer can improve the robustness of the U-Net model, and the optimized U-Net model had the highest dice similarity coefficient (DSC) (98.45%) and mean average precision (MAP) (0.88) for the liver tumor segmentation.