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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 Workshops pp 412–428Cite as

Self-supervised Orientation-Guided Deep Network for Segmentation of Carbon Nanotubes in SEM Imagery

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13808))

Abstract

Electron microscopy images of carbon nanotube (CNT) forests are difficult to segment due to the long and thin nature of the CNTs; density of the CNT forests resulting in CNTs touching, crossing, and occluding each other; and low signal-to-noise ratio electron microscopy imagery. In addition, due to image complexity, it is not feasible to prepare training segmentation masks. In this paper, we propose CNTSegNet, a dual loss, orientation-guided, self-supervised, deep learning network for CNT forest segmentation in scanning electron microscopy (SEM) images. Our training labels consist of weak segmentation labels produced by intensity thresholding of the raw SEM images and self labels produced by estimating orientation distribution of CNTs in these raw images. The proposed network extends a U-net-like encoder-decoder architecture with a novel two-component loss function. The first component is dice loss computed between the predicted segmentation maps and the weak segmentation labels. The second component is mean squared error (MSE) loss measuring the difference between the orientation histogram of the predicted segmentation map and the original raw image. Weighted sum of these two loss functions is used to train the proposed CNTSegNet network. The dice loss forces the network to perform background-foreground segmentation using local intensity features. The MSE loss guides the network with global orientation features and leads to refined segmentation results. The proposed system needs only a few-shot dataset for training. Thanks to it’s self-supervised nature, it can easily be adapted to new datasets.

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Acknowledgement

This work was partially supported by the National Science Foundation under award number CMMI-2026847. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Columbia, USA

    Nguyen P. Nguyen, Prasad Calyam, Kannappan Palaniappan & Filiz Bunyak

  2. Department of Mechanical and Aerospace Engineering, University of Missouri-Columbia, Columbia, MO, USA

    Ramakrishna Surya & Matthew Maschmann

Authors
  1. Nguyen P. Nguyen
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  2. Ramakrishna Surya
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  4. Prasad Calyam
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Corresponding author

Correspondence to Filiz Bunyak .

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Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Nguyen, N.P., Surya, R., Maschmann, M., Calyam, P., Palaniappan, K., Bunyak, F. (2023). Self-supervised Orientation-Guided Deep Network for Segmentation of Carbon Nanotubes in SEM Imagery. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13808. Springer, Cham. https://doi.org/10.1007/978-3-031-25085-9_24

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  • DOI: https://doi.org/10.1007/978-3-031-25085-9_24

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