Deep-Motion-Net: GNN-based volumetric liver shape reconstruction from single-view 2D projections - Takeaways - MDSpire
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Volumetric Reconstruction of Liver Shape from Single-View 2D Projections Using a GNN Approach: Deep-Motion-Net

  • By

  • Isuru Wijesinghe

  • Michael Nix

  • Arezoo Zakeri

  • Alireza Hokmabadi

  • Bashar Al-Qaisieh

  • Ali Gooya

  • Zeike Taylor

  • May 13, 2026

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  • 1

    Deep-Motion-Net utilizes a GNN to reconstruct 3D liver shapes from single-view kV X-ray images at arbitrary gantry angles.

  • 2

    The model integrates CNN-extracted features with mesh node displacements, enabling end-to-end training for patient-specific organ meshes.

  • 3

    Projection angle information is encoded as an additional input channel, allowing the model to learn angle-dependent anatomical features.

  • 4

    The architecture includes a 2D CNN encoder and a GNN-based mesh deformation network to enhance the accuracy of volumetric reconstructions.

  • 5

    Deep-Motion-Net addresses limitations of existing methods by reconstructing 3D anatomy from limited-FOV projections without fixed angles.

Original Source(s)

Deep-Motion-Net: GNN-based volumetric liver shape reconstruction from single-view 2D projections

  • by Isuru Wijesinghe, Michael Nix, Arezoo Zakeri, Alireza Hokmabadi, Bashar Al-Qaisieh, Ali Gooya, Zeike Taylor

  • May 13, 2026

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