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Tubular shape aware data generation for segmentation in medical imaging

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like objects, such as puncture needles, guiding sheaths, wires, and catheters. Detection and precise localization of these tube-like objects in the X-ray images are, therefore, of utmost value, catalyzing the development of accurate target-specific segmentation algorithms. Similar to the other medical imaging tasks, the manual pixel-wise annotation of the tubes is a resource-consuming process.

Methods

In this work, we aim to alleviate the lack of annotated images by using artificial data. Specifically, we present an approach for synthetic generation of the tube-shaped objects, with a generative adversarial network being regularized with a prior-shape constraint. Namely, our model uses Frangi-based regularization to draw synthetic tubes in the predefined fake mask regions and, then, uses the adversarial component to preserve the global realistic appearance of the synthesized image.

Results

Our method eliminates the need for the paired image–mask data and requires only a weakly labeled dataset, with fine-tuning on a small paired sample (10–20 images) proving sufficient to reach the accuracy of the fully supervised models.

Conclusion

We report the applicability of the approach for the task of segmenting tubes and catheters in the X-ray images, whereas the results should also hold for the other acquisition modalities and image computing applications that contain tubular objects.

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Notes

  1. Our implementation: https://github.com/ilyas-sid/SoftFrangiFilter2D.

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Acknowledgements

We acknowledge support from the Zhores computational cluster team [27].

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Authors and Affiliations

  1. Philips Research, 42 Bol’shoy blvd, Moscow, Russia, 121205

    Ilyas Sirazitdinov

  2. Philips Research, Philips GmbH Innovative Technologies, Röntgenstraße 24, 22335, Hamburg, Germany

    Heinrich Schulz, Axel Saalbach & Steffen Renisch

  3. Skoltech, Bol’shoy blvd 30/1, Moscow, Russia, 121205

    Ilyas Sirazitdinov & Dmitry V. Dylov

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  1. Ilyas Sirazitdinov
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  2. Heinrich Schulz
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  3. Axel Saalbach
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  4. Steffen Renisch
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  5. Dmitry V. Dylov
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Corresponding author

Correspondence to Dmitry V. Dylov.

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At time of conducting this study, all authors were associated with Philips Research. The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors. This research study was conducted retrospectively using human subject data made available in open access by [20]. Ethical approval was not required as confirmed by the license attached with the open access data.

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Sirazitdinov, I., Schulz, H., Saalbach, A. et al. Tubular shape aware data generation for segmentation in medical imaging. Int J CARS 17, 1091–1099 (2022). https://doi.org/10.1007/s11548-022-02621-3

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  • DOI: https://doi.org/10.1007/s11548-022-02621-3

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