IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0311080.html
   My bibliography  Save this article

EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation

Author

Listed:
  • Dhirendra Prasad Yadav
  • Bhisham Sharma
  • Julian L Webber
  • Abolfazl Mehbodniya
  • Shivank Chauhan

Abstract

Accurate segmentation of lung lesions in CT-scan images is essential to diagnose lung cancer. The challenges in lung nodule diagnosis arise due to their small size and diverse nature. We designed a transformer-based model EDTNet (Encoder Decoder Transformer Network) for PNS (Pulmonary Nodule Segmentation). Traditional CNN-based encoders and decoders are hindered by their inability to capture long-range spatial dependencies, leading to suboptimal performance in complex object segmentation tasks. To address the limitation, we leverage an enhanced spatial attention-based Vision Transformer (ViT) as an encoder and decoder in the EDTNet. The EDTNet integrates two successive transformer blocks, a patch-expanding layer, down-sampling layers, and up-sampling layers to improve segmentation capabilities. In addition, ESLA (Enhanced spatial aware local attention) and EGLA (Enhanced global aware local attention) blocks are added to provide attention to the spatial features. Furthermore, skip connections are introduced to facilitate symmetrical interaction between the corresponding encoder and decoder layer, enabling the retrieval of intricate details in the output. The EDTNet performance is compared with several models on DS1 and DS2, including Unet, ResUNet++, U-NET 3+, DeepLabV3+, SegNet, Trans-Unet, and Swin-UNet, demonstrates superior quantitative and visual results. On DS1, the EDTNet achieved 96.27%, 95.81%, 96.15% precision, IoU (Intersection over Union), and DSC (Sorensen–Dice coefficient). Moreover, the model has demonstrated sensitivity, IoU and SDC of 98.84%, 96.06% and 97.85% on DS2.

Suggested Citation

  • Dhirendra Prasad Yadav & Bhisham Sharma & Julian L Webber & Abolfazl Mehbodniya & Shivank Chauhan, 2024. "EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-23, November.
  • Handle: RePEc:plo:pone00:0311080
    DOI: 10.1371/journal.pone.0311080
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0311080
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0311080&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0311080?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. Sergey P. Primakov & Abdalla Ibrahim & Janita E. Timmeren & Guangyao Wu & Simon A. Keek & Manon Beuque & Renée W. Y. Granzier & Elizaveta Lavrova & Madeleine Scrivener & Sebastian Sanduleanu & Esma Ka, 2022. "Automated detection and segmentation of non-small cell lung cancer computed tomography images," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Muazzam Maqsood & Sadaf Yasmin & Irfan Mehmood & Maryam Bukhari & Mucheol Kim, 2021. "An Efficient DA-Net Architecture for Lung Nodule Segmentation," Mathematics, MDPI, vol. 9(13), pages 1-16, June.
    3. Michela Antonelli & Annika Reinke & Spyridon Bakas & Keyvan Farahani & Annette Kopp-Schneider & Bennett A. Landman & Geert Litjens & Bjoern Menze & Olaf Ronneberger & Ronald M. Summers & Bram Ginneken, 2022. "The Medical Segmentation Decathlon," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    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. Emmons, Karen M. & Mendez, Samuel & Lee, Rebekka M. & Erani, Diana & Mascioli, Lynette & Abreu, Marlene & Adams, Susan & Daly, James & Bierer, Barbara E., 2023. "Data sharing in the context of community-engaged research partnerships," Social Science & Medicine, Elsevier, vol. 325(C).
    2. Li, Shengxiao (Alex), 2023. "Revisiting the relationship between information and communication technologies and travel behavior: An investigation of older Americans," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    3. Maclean, Johanna Catherine & Tello-Trillo, Sebastian & Webber, Douglas, 2023. "Losing insurance and psychiatric hospitalizations," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 508-527.
    4. Sheeba J. Sujit & Muhammad Aminu & Tatiana V. Karpinets & Pingjun Chen & Maliazurina B. Saad & Morteza Salehjahromi & John D. Boom & Mohamed Qayati & James M. George & Haley Allen & Mara B. Antonoff &, 2024. "Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Elizaveta Sivak & Paulina Pankowska & Adriënne Mendrik & Tom Emery & Javier Garcia-Bernardo & Seyit Höcük & Kasia Karpinska & Angelica Maineri & Joris Mulder & Malvina Nissim & Gert Stulp, 2024. "Combining the strengths of Dutch survey and register data in a data challenge to predict fertility (PreFer)," Journal of Computational Social Science, Springer, vol. 7(2), pages 1403-1431, October.
    6. Jun Ma & Yuting He & Feifei Li & Lin Han & Chenyu You & Bo Wang, 2024. "Segment anything in medical images," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Philipp Goebl & Jed Wingrove & Omar Abdelmannan & Barbara Brito Vega & Jonathan Stutters & Silvia Da Graca Ramos & Owain Kenway & Thomas Rossor & Evangeline Wassmer & Douglas L. Arnold & D. Louis Coll, 2025. "Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research," Nature Communications, Nature, vol. 16(1), pages 1-15, December.

    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:pone00:0311080. 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.