ReViTA-Unet: An Enhanced Semantic Segmentation Model for Automated Morphometric Analysis of Macrobrachium rosenbergii.

Sun D, Chen Q, Yu G, Han X, Li C, Zhou C, Ye H

Open source

DOI: 10.3390/s26144570
Published: 2026 Jul 19
Container: Sensors (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s26144570,
  title = {ReViTA-Unet: An Enhanced Semantic Segmentation Model for Automated Morphometric Analysis of Macrobrachium rosenbergii.},
  author = {Sun D and Chen Q and Yu G and Han X and Li C and Zhou C and Ye H},
  year = {2026},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s26144570},
  url = {https://doi.org/10.3390/s26144570}
}

RIS

TY  - JOUR
TI  - ReViTA-Unet: An Enhanced Semantic Segmentation Model for Automated Morphometric Analysis of Macrobrachium rosenbergii.
AU  - Sun D
AU  - Chen Q
AU  - Yu G
AU  - Han X
AU  - Li C
AU  - Zhou C
AU  - Ye H
PY  - 2026
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s26144570
UR  - https://doi.org/10.3390/s26144570
ER  -

APA

D, S., Q, C., G, Y., X, H., C, L., C, Z., & H, Y. (2026). ReViTA-Unet: An Enhanced Semantic Segmentation Model for Automated Morphometric Analysis of Macrobrachium rosenbergii.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26144570

Source records

pubmed · retrieved 2026-09-25T10:15:23.034Z
europe-pmc · retrieved 2026-09-25T10:15:23.061Z
doaj · retrieved 2026-09-25T10:15:23.030Z