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Robust unsupervised texture segmentation for motion analysis in ultrasound images

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

Abstract

Purpose

Ultrasound imaging has emerged as a promising cost-effective and portable non-irradiant modality for the diagnosis and follow-up of diseases. Motion analysis can be performed by segmenting anatomical structures of interest before tracking them over time. However, doing so in a robust way is challenging as ultrasound images often display a low contrast and blurry boundaries.

Methods

In this paper, a robust descriptor inspired from the fractal dimension is presented to locally characterize the gray-level variations of an image. This descriptor is an adaptive grid pattern whose scale locally varies as the gray-level variations of the image. Robust features are then located based on the gray-level variations, which are more likely to be consistently tracked over time despite the presence of noise.

Results

The method was validated on three datasets: segmentation of the left ventricle on simulated echocardiography (Dice coefficient, DC), accuracy of diaphragm motion tracking for healthy subjects (mean sum of distances, MSD) and for a scoliosis patient (root mean square error, RMSE). Results show that the method segments the left ventricle accurately (\(\textrm{DC}=0.84\)) and robustly tracks the diaphragm motion for healthy subjects (\(\textrm{MSD}=1.10\) mm) and for the scoliosis patient (\(\textrm{RMSE}=1.22\) mm).

Conclusions

This method has the potential to segment structures of interest according to their texture in an unsupervised fashion, as well as to help analyze the deformation of tissues. Possible applications are not limited to US image. The same principle could also be applied to other medical imaging modalities such as MRI or CT scans.

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Acknowledgements

This project was funded by the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research.

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Authors and Affiliations

  1. Department of Electrical Engineering, École de technologie supérieure, 1100, Rue Notre-Dame Ouest, Montreal, QC, H3C 1K3, Canada

    Arnaud Brignol & Catherine Laporte

  2. Department of Computer Engineering and Software Engineering, Polytechnique Montréal, 2900, boul. Édouard-Montpetit, Montreal, QC, H3T 1J4, Canada

    Farida Cheriet

  3. Centre de réadaptation Marie-Enfant du CHU Sainte-Justine, 5200, rue Bélanger Est, Montreal, QC, H1T 1C9, Canada

    Jean-François Aubin-Fournier

  4. Faculté de médecine, École de réadaptation, 6128, succursale Centre-ville, Montreal, QC, H3C 3J7, Canada

    Carole Fortin

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  1. Arnaud Brignol
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Correspondence to Arnaud Brignol.

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The authors declare to have no conflict of interest. For CRME data, the study was approved by the Research Ethics Board of Sainte-Justine Hospital (Montreal, Canada) and the patient signed an informed consent form.

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Brignol, A., Cheriet, F., Aubin-Fournier, JF. et al. Robust unsupervised texture segmentation for motion analysis in ultrasound images. Int J CARS 20, 97–106 (2025). https://doi.org/10.1007/s11548-024-03249-1

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  • DOI: https://doi.org/10.1007/s11548-024-03249-1

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