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Communication Dans Un Congrès Année : 2023

Trackerless Volume Reconstruction from Intraoperative Ultrasound Images

Résumé

This paper proposes a method for trackerless ultrasound volume reconstruction in the context of minimally invasive surgery. It is based on a Siamese architecture, including a recurrent neural network that leverages the ultrasound image features and the optical flow to estimate the relative position of frames. Our method does not use any additional sensor and was evaluated on ex-vivo porcine data. It achieves translation and orientation errors of 0.449 ± 0.189 mm and 1.3 ± 1.5 degrees respectively for the relative pose estimation. In addition, despite the predominant non-linearity motion in our context, our method achieves a good reconstruction with final and average drift rates of 23.11% and 28.71% respectively. To the best of our knowledge, this is the first work to address volume reconstruction in the context of intravascular ultrasound. Code will be publicly released.
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Dates et versions

hal-04108248 , version 1 (26-05-2023)
hal-04108248 , version 2 (18-07-2023)
hal-04108248 , version 3 (20-07-2023)

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Identifiants

  • HAL Id : hal-04108248 , version 3

Citer

Sidaty El hadramy, Juan Verde, Karl-Philippe Beaudet, Nicolas Padoy, Stéphane Cotin. Trackerless Volume Reconstruction from Intraoperative Ultrasound Images. MICCAI 2023, Oct 2023, Vancouver, Canada. ⟨hal-04108248v3⟩
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