PMID- 35295165
OWN - NLM
STAT- MEDLINE
DCOM- 20220502
LR  - 20220502
IS  - 2040-2309 (Electronic)
IS  - 2040-2295 (Print)
IS  - 2040-2295 (Linking)
VI  - 2022
DP  - 2022
TI  - 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.
PG  - 3951112
LID - 10.1155/2022/3951112 [doi]
LID - 3951112
AB  - 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 mumol/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.
CI  - Copyright (c) 2022 Haiyan Zhao et al.
FAU - Zhao, Haiyan
AU  - Zhao H
AD  - The Elderly of Treatment Department of Critical Medicine, The Frist Affiliated 
      Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
FAU - Wang, Yuping
AU  - Wang Y
AD  - The Elderly of Treatment Department of Critical Medicine, The Frist Affiliated 
      Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
FAU - He, Chen
AU  - He C
AD  - The Elderly of Treatment Department of Critical Medicine, The Frist Affiliated 
      Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
FAU - Yang, Jilin
AU  - Yang J
AD  - The Elderly of Treatment Department of Critical Medicine, The Frist Affiliated 
      Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
FAU - Shi, Yaoming
AU  - Shi Y
AD  - Undergraduate Clinical Major, Haiyuan College of Kunming Medical University, 
      Kunming, Yunnan 65003, China.
FAU - Zhu, Xiaolin
AU  - Zhu X
AUID- ORCID: 0000-0001-5169-1030
AD  - The Elderly of Treatment Department of Critical Medicine, The Frist Affiliated 
      Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
LA  - eng
PT  - Journal Article
DEP - 20220307
PL  - England
TA  - J Healthc Eng
JT  - Journal of healthcare engineering
JID - 101528166
RN  - KA46RNI6HN (Emodin)
SB  - IM
MH  - Apoptosis
MH  - *Deep Learning
MH  - *Emodin/pharmacology
MH  - Hep G2 Cells
MH  - Humans
MH  - Image Processing, Computer-Assisted
MH  - *Liver Neoplasms/diagnostic imaging/drug therapy
MH  - Magnetic Resonance Imaging
PMC - PMC8920667
COIS- The authors declare that they have no conflicts of interest regarding this work.
EDAT- 2022/03/18 06:00
MHDA- 2022/05/03 06:00
PMCR- 2022/03/07
CRDT- 2022/03/17 05:05
PHST- 2021/12/31 00:00 [received]
PHST- 2022/01/26 00:00 [accepted]
PHST- 2022/03/17 05:05 [entrez]
PHST- 2022/03/18 06:00 [pubmed]
PHST- 2022/05/03 06:00 [medline]
PHST- 2022/03/07 00:00 [pmc-release]
AID - 10.1155/2022/3951112 [doi]
PST - epublish
SO  - J Healthc Eng. 2022 Mar 7;2022:3951112. doi: 10.1155/2022/3951112. eCollection 
      2022.