Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.

Gong Z, Song J, Guo W, Ju R, Zhao D, Tan W, Zhou W, Zhang G

DOI: 10.3934/mbe.2022655
Published: 2022 Sep 23
Container: Mathematical biosciences and engineering : MBE
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3934/mbe.2022655,
  title = {Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.},
  author = {Gong Z and Song J and Guo W and Ju R and Zhao D and Tan W and Zhou W and Zhang G},
  year = {2022},
  journal = {Mathematical biosciences and engineering : MBE},
  doi = {10.3934/mbe.2022655},
  url = {https://doi.org/10.3934/mbe.2022655}
}

RIS

TY  - JOUR
TI  - Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.
AU  - Gong Z
AU  - Song J
AU  - Guo W
AU  - Ju R
AU  - Zhao D
AU  - Tan W
AU  - Zhou W
AU  - Zhang G
PY  - 2022
JO  - Mathematical biosciences and engineering : MBE
DO  - 10.3934/mbe.2022655
UR  - https://doi.org/10.3934/mbe.2022655
ER  -

APA

Z, G., J, S., W, G., R, J., D, Z., W, T., W, Z., & G, Z. (2022). Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.. Mathematical biosciences and engineering : MBE. https://doi.org/10.3934/mbe.2022655

Source records

pubmed · retrieved 2026-11-10T08:07:28.436Z