Turn off MathJax
Article Contents
Article Navigation >  Chinese Journal of Space Science  > 2025  > Corrected proofOpen Access
LI Xinze, CAI Yujia, YU Qiang. Segmentation Algorithm of X-ray Microstructure Image of Na6Mo11O36 Material (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1532-1541 doi: 10.11728/cjss2025.06.2024-0185
Citation: LI Xinze, CAI Yujia, YU Qiang. Segmentation Algorithm of X-ray Microstructure Image of Na6Mo11O36 Material (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1532-1541 doi: 10.11728/cjss2025.06.2024-0185
shu

Segmentation Algorithm of X-ray Microstructure Image of Na6Mo11O36 Material

doi: 10.11728/cjss2025.06.2024-0185 cstr: 32142.14.cjss.2024-0185
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049

  • 3.

    Department of Electronic Engineering, Guilin Institute of Information Technology, Guilin 541199

  • Received Date: 2024-12-13
  • Rev Recd Date: 2025-05-13
    • Available Online: 2025-05-15
    Abstract
  • Conducting materials science experiments in a microgravity environment mitigates the influence of gravity, enabling the study of intrinsic material growth mechanisms and the fabrication of materials with enhanced properties. The high-temperature materials research rack aboard the Chinese Space Station is equipped with an X-ray transmission imaging module, facilitating real-time imaging and observation of material solidification processes under microgravity. However, due to the constraints of the space station’s experimental conditions, the X-ray images acquired by this module often exhibit blurriness, making direct observation of microstructures challenging. To address this issue, the GFF-UNet++ image segmentation algorithm is proposed, specifically tailored for analyzing the microstructures formed during the solidification of Na6Mo11O36 material. The algorithm’s effectiveness is rigorously evaluated in terms of both image segmentation performance and its relevance to materials science applications. The experimental results demonstrate that in the image segmentation task, GFF-UNet++ outperforms established algorithms such as UNet, UNet++, DC-UNet, UNet3+ and Pretrained-Microscopy-Models, achieving notable improvements across various image segmentation metrics. Furthermore, the microstructures formed during the growth of the Na6Mo11O36 can be segmented more accurately. This provides new ideas and methods for the study of the microstructure segmentation of materials, and has important application value.

     

    • FullText(HTML)
  • loading
    • References(24)
  • [1]
    WANG Y, LI S, LIU Z, et al. Anisotropy-dependent seaweed growth during directional solidification of Al-4.5%Cu single crystal[J]. Scripta Materialia, 2020, 186: 121-126 doi: 10.1016/j.scriptamat.2020.05.006
    [2]
    AGHALARI M, AGHAGOLZADEH A, EZOJI M. Brain tumor image segmentation via asymmetric/symmetric UNet based on two-pathway-residual blocks[J]. Biomedical Signal Processing and Control, 2021, 69(6): 102841
    [3]
    WANG E K, CHEN C-M, HASSAN M M, et al. A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain[J]. Future Generation Computer Systems, 2020, 108: 135-144 doi: 10.1016/j.future.2020.02.054
    [4]
    HUANG H, LIN L, TONG R, et al. UNet 3+: A full-scale connected UNet for medical image segmentation[C]// 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE, 2020: 1055-1059
    [5]
    SONG Y, QU Z, LIAO H, et al. Material twins generation of woven polymer composites based on ResL-U-Net convolutional neural networks[J]. Composite Structures, 2023, 307: 116672 doi: 10.1016/j.compstruct.2023.116672
    [6]
    GUAN Y, PANG Z, SUN H, et al. Material strain image segmentation algorithm based on improved UNET network; proceedings of the 9th International Symposium on Test Automation & Instrumentation (ISTAI 2022), F 11-13 Nov. 2022, 2022 [C]
    [7]
    SHI P, DUAN M M, YANG L F, et al. An improved U-Net image segmentation method and its application for metallic grain size statistics[J]. MATERIALS, 2022, 15(13)
    [8]
    STUCKNER J, HARDER B, SMITH T M. Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset[J]. NPJ Computational Materials, 2022, 8(1): 200 doi: 10.1038/s41524-022-00878-5
    [9]
    IBTEHAZ N, RAHMAN M S. MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2019, 121: 74-87
    [10]
    GAO WEIZE, CHEN SHANXIONG, MO BOFENG, et al. R-UNet++: a local segmentation network for the classification of oracle bone materials[J]. Journal of Computer-Aided Design :Times New Roman;">& Computer Graphics, 2022, 34(3): 415-424
    [11]
    HIRABAYASHI Y, IGA H, OGAWA H, et al. Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials[J]. NPJ Computational Materials, 2024, 10(1): 46 doi: 10.1038/s41524-024-01226-5
    [12]
    HORWATH J P, ZAKHAROV D N, MéGRET R, et al. Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images[J]. NPJ Computational Materials, 2020, 6(1): 108 doi: 10.1038/s41524-020-00363-x
    [13]
    YANG B, WU M, TEIZER W. Modified UNet++ with attention gate for graphene identification by optical microscopy[J]. Carbon: An International Journal Sponsored by the American Carbon Society, 2022, 195: 246-252
    [14]
    LIU P, SONG Y, CHAI M, et al. Swin–UNet++: a nested swin transformer architecture for location identification and morphology segmentation of dimples on 2.25Cr1Mo0.25V fractured surface[J]. Materials, 2021, 14(24): 7504 doi: 10.3390/ma14247504
    [15]
    SHU K Y, CHEN Z X, ZHU B, et al. Unsupervised Segmentation for Microstructure Identification of High Strength Steel with Superpixel Segmentation and Texture Feature Clustering; proceedings of the Proceedings of the 14th International Conference on the Technology of Plasticity - Current Trends in the Technology of Plasticity, Cham, F 2024//, 2024 [C]. Springer Nature Switzerland
    [16]
    RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[J]. Springer, Cham, 2015. DOI: 10.1007/978-3-662-54345-0_3
    [17]
    ZHOU Z, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al. Unet++: a nested U-Net architecture for medical image segmentation[J]. 2018. DOI: 10.1007/978-3-030-00889-5_1
    [18]
    RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation; proceedings of the medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, F, 2015 [C]. Springer
    [19]
    ZHOU Z, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. [C]//proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Cham, F 2018. Springer International Publishing. DOI: 10.1007/978-3-030-00889-5_1
    [20]
    LOU A, GUAN S, LOEW M H. DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation[J]. Proceedings of SPIE, 2021, 11596(000). DOI: 10.1117/12.2582338
    [21]
    HUANG H, LIN L, TONG R, et al. UNet 3+: a full-scale connected UNet for medical image segmentation[J]. arXiv, 2020. DOI: 10.1109/ICASSP40776.2020.9053405
    [22]
    ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature Methods, 2021, 18(2): 203-211 doi: 10.1038/s41592-020-01008-z
    [23]
    LOU A, GUAN S, LOEW M. DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation[C]//Image Processing. SPIE, 2021. DOI: 10.1117/12.2582338
    [24]
    HUANG H, LIN L, TONG R, et al. Unet 3+: a full-scale connected unet for medical image segmentation[J]. arXiv, 2020. DOI: 10.1109/ICASSP40776.2020.9053405
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(271) PDF Downloads(10) Cited by()
    Proportional views
    Related

    Journal Introduction

    ISSN 0254-6124（网络）       CN 11-1783/V

    Copyright © Editorial Office of Chinese Journal of Space Science.  京ICP备08100722号

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    x Close Forever Close

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return