IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0304702.html

PathEX: Make good choice for whole slide image extraction

Author

Listed:
  • Xinda Yang
  • Ranze Zhang
  • Yuan Yang
  • Yu Zhang
  • Kai Chen

Abstract

Background: The tile-based approach has been widely used for slide-level predictions in whole slide image (WSI) analysis. However, the irregular shapes and variable dimensions of tumor regions pose challenges for the process. To address this issue, we proposed PathEX, a framework that integrates intersection over tile (IoT) and background over tile (BoT) algorithms to extract tile images around boundaries of annotated regions while excluding the blank tile images within these regions. Methods: We developed PathEX, which incorporated IoT and BoT into tile extraction, for training a classification model in CAM (239 WSIs) and PAIP (40 WSIs) datasets. By adjusting the IoT and BoT parameters, we generated eight training sets and corresponding models for each dataset. The performance of PathEX was assessed on the testing set comprising 13,076 tile images from 48 WSIs of CAM dataset and 6,391 tile images from 10 WSIs of PAIP dataset. Results: PathEX could extract tile images around boundaries of annotated region differently by adjusting the IoT parameter, while exclusion of blank tile images within annotated regions achieved by setting the BoT parameter. As adjusting IoT from 0.1 to 1.0, and 1—BoT from 0.0 to 0.5, we got 8 train sets. Experimentation revealed that set C demonstrates potential as the most optimal candidate. Nevertheless, a combination of IoT values ranging from 0.2 to 0.5 and 1-BoT values ranging from 0.2 to 0.5 also yielded favorable outcomes. Conclusions: In this study, we proposed PathEX, a framework that integrates IoT and BoT algorithms for tile image extraction at the boundaries of annotated regions while excluding blank tiles within these regions. Researchers can conveniently set the thresholds for IoT and BoT to facilitate tile image extraction in their own studies. The insights gained from this research provide valuable guidance for tile image extraction in digital pathology applications.

Suggested Citation

  • Xinda Yang & Ranze Zhang & Yuan Yang & Yu Zhang & Kai Chen, 2024. "PathEX: Make good choice for whole slide image extraction," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0304702
    DOI: 10.1371/journal.pone.0304702
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0304702
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0304702&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0304702?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chan, Jianpeng & Papaioannou, Iason & Straub, Daniel, 2024. "Bayesian improved cross entropy method with categorical mixture models for network reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    2. Mattrand, C. & Bourinet, J.-M., 2014. "The cross-entropy method for reliability assessment of cracked structures subjected to random Markovian loads," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 171-182.
    3. R. Y. Rubinstein, 2005. "A Stochastic Minimum Cross-Entropy Method for Combinatorial Optimization and Rare-event Estimation," Methodology and Computing in Applied Probability, Springer, vol. 7(1), pages 5-50, March.
    4. Kin-Ping Hui, 2011. "Cooperative Cross-Entropy method for generating entangled networks," Annals of Operations Research, Springer, vol. 189(1), pages 205-214, September.
    5. Mathieu Balesdent & Jérôme Morio & Loïc Brevault, 2016. "Rare Event Probability Estimation in the Presence of Epistemic Uncertainty on Input Probability Distribution Parameters," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 197-216, March.
    6. Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi & Ngoduy, Dong & Watling, David, 2021. "Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    7. Xi Chen & Enlu Zhou, 2015. "Population model-based optimization," Journal of Global Optimization, Springer, vol. 63(1), pages 125-148, September.
    8. Lvyang Qiu & Shuyu Li & Yunsick Sung, 2021. "3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    9. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    10. Akimoto, Youhei & Auger, Anne & Hansen, Nikolaus, 2022. "An ODE method to prove the geometric convergence of adaptive stochastic algorithms," Stochastic Processes and their Applications, Elsevier, vol. 145(C), pages 269-307.
    11. Anastasia Spiliopoulou & Ioannis Papamichail & Markos Papageorgiou & Yannis Tyrinopoulos & John Chrysoulakis, 2017. "Macroscopic traffic flow model calibration using different optimization algorithms," Operational Research, Springer, vol. 17(1), pages 145-164, April.
    12. Huang, Zhenzhen & Kwok, Yue Kuen & Xu, Ziqing, 2024. "Efficient algorithms for calculating risk measures and risk contributions in copula credit risk models," Insurance: Mathematics and Economics, Elsevier, vol. 115(C), pages 132-150.
    13. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    14. Zhang, Yali & Shang, Pengjian, 2019. "Multivariate multiscale distribution entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 72-80.
    15. Chen, Siliang & Chen, Kang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2022. "Deep learning-based image recognition method for on-demand defrosting control to save energy in commercial energy systems," Applied Energy, Elsevier, vol. 324(C).
    16. A. Gouda & T. Szántai, 2008. "Rare event probabilities in stochastic networks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 16(4), pages 441-461, December.
    17. Liang Huang & Juanjuan Zhu & Mulan Qiu & Xiaoxiang Li & Shasha Zhu, 2022. "CA-BASNet: A Building Extraction Network in High Spatial Resolution Remote Sensing Images," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    18. Reuven Y. Rubinstein, 2006. "How Many Needles are in a Haystack, or How to Solve #P-Complete Counting Problems Fast," Methodology and Computing in Applied Probability, Springer, vol. 8(1), pages 5-51, March.
    19. Ad Ridder & Bruno Tuffin, 2012. "Probabilistic Bounded Relative Error Property for Learning Rare Event Simulation Techniques," Tinbergen Institute Discussion Papers 12-103/III, Tinbergen Institute.
    20. Mehni, Moien Barkhori & Mehni, Mohammad Barkhori, 2023. "Reliability analysis with cross-entropy based adaptive Markov chain importance sampling and control variates," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0304702. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.