Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images

Parin Kittipongdaja, Thitirat Siriborvornratanakul

Open source

DOI: 10.1186/s13640-022-00581-x
Published: 2022-03-22
Container: EURASIP Journal on Image and Video Processing
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1186/s13640-022-00581-x,
  title = {Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images},
  author = {Parin Kittipongdaja and Thitirat Siriborvornratanakul},
  year = {2022},
  journal = {EURASIP Journal on Image and Video Processing},
  doi = {10.1186/s13640-022-00581-x},
  url = {https://doi.org/10.1186/s13640-022-00581-x}
}

RIS

TY  - JOUR
TI  - Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images
AU  - Parin Kittipongdaja
AU  - Thitirat Siriborvornratanakul
PY  - 2022
JO  - EURASIP Journal on Image and Video Processing
DO  - 10.1186/s13640-022-00581-x
UR  - https://doi.org/10.1186/s13640-022-00581-x
ER  -

APA

Kittipongdaja, P., & Siriborvornratanakul, T. (2022). Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images. EURASIP Journal on Image and Video Processing. https://doi.org/10.1186/s13640-022-00581-x

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crossref · retrieved 2026-09-25T17:26:18.615Z