Condensed U-Net (CU-Net): An Improved U-Net Architecture for
Cell Segmentation Powered by 4x4 Max-Pooling Layers

D 2020

Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers

AKBAS, Cem Emre a Michal KOZUBEK

Základní údaje

Originální název

Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers

Autoři

AKBAS, Cem Emre (792 Turecko, domácí) a Michal KOZUBEK (203 Česká republika, garant, domácí)

Vydání

Iowa, IEEE 17th International Symposium on Biomedical Imaging, od s. 446-450, 5 s. 2020

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/20:00115503

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5386-9330-8

ISSN

DOI

http://dx.doi.org/10.1109/ISBI45749.2020.9098351

UT WoS

000578080300083

Klíčová slova anglicky

Biomedical Image Segmentation; Convolutional Neural Networks; Deep Learning; Feature Learning; Max Pooling

Štítky

cbia-web, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 5. 2021 05:41, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Recently, the U-Net has been the dominant approach in the cell segmentation task in biomedical images due to its success in a wide range of image recognition tasks. However, recent studies did not focus enough on updating the architecture of the U-Net and designing specialized loss functions for bioimage segmentation. We show that the U-Net architecture can achieve more successful results with efficient architectural improvements. We propose a condensed encoder-decoder scheme that employs the 4x4 max-pooling operation and triple convolutional layers. The proposed network architecture is trained using a novel combined loss function specifically designed for bioimage segmentation. On the benchmark datasets from the Cell Tracking Challenge, the experimental results show that the proposed cell segmentation system outperforms the U-Net.

Návaznosti

EF16_013/0001775, projekt VaV

Název: Modernizace a podpora výzkumných aktivit národní infrastruktury pro biologické a medicínské zobrazování Czech-BioImaging

LTC17016, projekt VaV

Název: Benchmarking algoritmů segmentace a sledování buněk

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Benchmarking algoritmů segmentace a sledování buněk, INTER-COST

MUNI/A/1050/2019, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX (Akronym: SV-FI MAV IX)

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

AKBAS, Cem Emre a Michal KOZUBEK. Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers. Online. In IEEE 17th International Symposium on Biomedical Imaging. Iowa: IEEE, 2020, s. 446-450. ISBN 978-1-5386-9330-8. Dostupné z: https://dx.doi.org/10.1109/ISBI45749.2020.9098351.

@inproceedings{1644098,
   author = {Akbas, Cem Emre and Kozubek, Michal},
   address = {Iowa},
   booktitle = {IEEE 17th International Symposium on Biomedical Imaging},
   doi = {http://dx.doi.org/10.1109/ISBI45749.2020.9098351},
   keywords = {Biomedical Image Segmentation; Convolutional Neural Networks; Deep Learning; Feature Learning; Max Pooling},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Iowa},
   isbn = {978-1-5386-9330-8},
   pages = {446-450},
   publisher = {IEEE},
   title = {Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers},
   url = {https://ieeexplore.ieee.org/abstract/document/9098351},
   year = {2020}
}

TY  - JOUR
ID  - 1644098
AU  - Akbas, Cem Emre - Kozubek, Michal
PY  - 2020
TI  - Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers
PB  - IEEE
CY  - Iowa
SN  - 9781538693308
KW  - Biomedical Image Segmentation
KW  - Convolutional Neural Networks
KW  - Deep Learning
KW  - Feature Learning
KW  - Max Pooling
UR  - https://ieeexplore.ieee.org/abstract/document/9098351
L2  - https://ieeexplore.ieee.org/abstract/document/9098351
N2  - Recently, the U-Net has been the dominant approach in the cell segmentation task in biomedical images due to its success in a wide range of image recognition tasks. However, recent studies did not focus enough on updating the architecture of the U-Net and designing specialized loss functions for bioimage segmentation. We show that the U-Net architecture can achieve more successful results with efficient architectural improvements. We propose a condensed encoder-decoder scheme that employs the 4x4 max-pooling operation and triple convolutional layers. The proposed network architecture is trained using a novel combined loss function specifically designed for bioimage segmentation. On the benchmark datasets from the Cell Tracking Challenge, the experimental results show that the proposed cell segmentation system outperforms the U-Net.
ER  -

AKBAS, Cem Emre a Michal KOZUBEK. Condensed U-Net (CU-Net): An Improved U-Net Architecture for Cell Segmentation Powered by 4x4 Max-Pooling Layers. Online. In \textit{IEEE 17th International Symposium on Biomedical Imaging}. Iowa: IEEE, 2020, s.~446-450. ISBN~978-1-5386-9330-8. Dostupné z: https://dx.doi.org/10.1109/ISBI45749.2020.9098351.

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