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
  • [21] Unsupervised Image-to-Image Translation Networks
    Liu, Ming-Yu
    Breuel, Thomas
    Kautz, Jan
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
  • [22] Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images
    Andrade, Catarina
    Teixeira, Luis F.
    Vasconcelos, Maria Joao M.
    Rosado, Luis
    JOURNAL OF IMAGING, 2021, 7 (01)
  • [23] Image-to-Image Translation using a Relativistic Generative Adversarial Network
    Xing, Xingrun
    Zhang, Dawei
    ELEVENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2019), 2019, 11179
  • [24] Synthesizing and Manipulating Natural Videos Using Image-to-Image Translation
    Yeh, Ryan
    Loui, Alexander
    2021 IEEE WESTERN NEW YORK IMAGE AND SIGNAL PROCESSING WORKSHOP (WNYISPW), 2021,
  • [25] Object Detection to Evaluate Image-to-Image Translation on Different Road Conditions
    Sudo, Fumiya
    Hashimoto, Yoshihiro
    Lisi, Giuseppe
    INTELLIGENT HUMAN SYSTEMS INTEGRATION 2020, 2020, 1131 : 143 - 149
  • [26] Hand Hygiene Quality Assessment Using Image-to-Image Translation
    Wang, Chaofan
    Yang, Kangning
    Jiang, Weiwei
    Wei, Jing
    Sarsenbayeva, Zhanna
    Goncalves, Jorge
    Kostakos, Vassilis
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VII, 2022, 13437 : 64 - 73
  • [27] Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
    Sela, Matan
    Richardson, Elad
    Kimmel, Ron
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, : 1585 - 1594
  • [28] Eliminating Adversarial Perturbations Using Image-to-Image Translation Method
    Zhang, Haibo
    Yao, Zhihua
    Sakurai, Kouichi
    APPLIED CRYPTOGRAPHY AND NETWORK SECURITY WORKSHOPS, ACNS 2023 SATELLITE WORKSHOPS, ADSC 2023, AIBLOCK 2023, AIHWS 2023, AIOTS 2023, CIMSS 2023, CLOUD S&P 2023, SCI 2023, SECMT 2023, SIMLA 2023, 2023, 13907 : 601 - 620
  • [29] DehazeGAN: Underwater Haze Image Restoration using Unpaired Image-to-image Translation
    Cho, Younggun
    Malav, Ramavtar
    Pandey, Gaurav
    Kim, Ayoung
    IFAC PAPERSONLINE, 2019, 52 (21): : 82 - 85
  • [30] A novel framework for image-to-image translation and image compression
    Yang, Fei
    Wang, Yaxing
    Herranz, Luis
    Cheng, Yongmei
    Mozerov, Mikhail G.
    NEUROCOMPUTING, 2022, 508 : 58 - 70