Cell Segmentation by Image-to-Image Translation using Multiple Different Discriminators

被引:0
|
作者
Kato, Sota [1 ]
Hotta, Kazuhiro [1 ]
机构
[1] Meijo Univ, Tempaku Ku, 1-501 Shiogamaguchi, Nagoya, Aichi 4688502, Japan
关键词
Image to Image Translation; Semantic Segmentation; Cell Segmentation;
D O I
10.5220/0009170103300335
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper presents a cell image segmentation method by improving the pix2pix. Pix2pix improves the accuracy by competing a generator and a discriminator The relationship of generator and discriminator is likened as follows. A generator is a fraudster who creates a fake image to fool the discriminator. A discriminator is a police officer who checks the fake image created by the generator. If we increase the number of police officers and different police officers are used, they have different roles and various viewpoints are used to check the fake image. In experiments, we evaluate our method on segmentation problem of cell images. We compared our method with conventional pix2pix using one discriminator. As a result, the accuracy will be improved. Thus, we propose to use multiple different discriminators to improve the segmentation accuracy of pix2pix. We confirmed that our proposed method outperformed conventional pix2pix and pix2pix using multiple same discriminators.
引用
收藏
页码:330 / 335
页数:6
相关论文
共 50 条
  • [31] Correction to: Generative image completion with image-to-image translation
    Shuzhen Xu
    Qing Zhu
    Jin Wang
    Neural Computing and Applications, 2020, 32 : 17809 - 17809
  • [32] Cross-Modality LGE-CMR Segmentation Using Image-to-Image Translation Based Data Augmentation
    Wang, Wei
    Yu, Xinhua
    Fang, Bo
    Zhao, Yue
    Chen, Yongyong
    Wei, Wei
    Chen, Junxin
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2023, 20 (04) : 2367 - 2375
  • [33] Unsupervised image-to-image translation to reduce the annotation effort for instance segmentation of field vegetables
    Lueling, Nils
    Straub, Jonas
    Stana, Alexander
    Reiser, David
    Clar, Johannes
    Griepentrog, Hans W.
    SMART AGRICULTURAL TECHNOLOGY, 2024, 7
  • [34] Unsupervised Image-to-Image Translation with Generative Prior
    Yang, Shuai
    Jiang, Liming
    Liu, Ziwei
    Loy, Chen Change
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 18311 - 18320
  • [35] Leveraging Local Domains for Image-to-Image Translation
    Dell'Eva, Anthony
    Pizzati, Fabio
    Bertozzi, Massimo
    de Charette, Raoul
    PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5, 2022, : 179 - 189
  • [36] Image-to-Image Translation on Defined Highlighting Regions by Semi-Supervised Semantic Segmentation
    Chang, Ching-Yu
    Ye, Chun-Ting
    Wei, Tzer-Jen
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [37] Downscaling for Climate Data in Indonesia Using Image-to-Image Translation Approach
    Muttaqien, Furqon Hensan
    Rahadianti, Laksmita
    Latifah, Arnida L.
    13TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND INFORMATION SYSTEMS (ICACSIS 2021), 2021, : 73 - +
  • [38] Unsupervised Image-to-Image Translation with Style Consistency
    Lai, Binxin
    Wang, Yuan-Gen
    PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT VI, 2024, 14430 : 322 - 334
  • [39] Image-to-Image Translation with Conditional Adversarial Networks
    Isola, Phillip
    Zhu, Jun-Yan
    Zhou, Tinghui
    Efros, Alexei A.
    30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 5967 - 5976
  • [40] Breaking the Dilemma of Medical Image-to-image Translation
    Kong, Lingke
    Lian, Chenyu
    Huang, Detian
    Li, Zhenjiang
    Hu, Yanle
    Zhou, Qichao
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34