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Communication Dans Un Congrès Année : 2024

Beyond respiratory models: a physics-enhanced synthetic data generation method for 2D-3D deformable registration

Résumé

Deformable image registration is crucial in aligning medical images for various clinical applications, yet enhancing its efficiency and robustness remains a challenge. Deep Learning methods have shown very promising results for addressing the registration process, however, acquiring sufficient and diverse data for training remains a hurdle. Synthetic data generation strategies have emerged as a solution, yet existing methods often lack versatility and often do not represent well certain types of deformation. This work focuses on X-ray to CT 2D-3D deformable image registration for abdominal interventions, where tissue deformation can arise from multiple sources. Due to the scarcity of real-world data for this task, synthetic data generation is unavoidable. Unlike previous approaches relying on statistical models extracted from 4DCT images, our method leverages a single 3D CT image and physically corrected randomized Displacement Vector Fields (DVF) to enable 2D-3D registration for a variety of clinical scenarios. We believe that our approach represents a significant step towards overcoming data scarcity challenges and enhancing the effectiveness of DL-based DIR in a variety of clinical settings.
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Dates et versions

hal-04599413 , version 1 (17-06-2024)

Identifiants

  • HAL Id : hal-04599413 , version 1

Citer

Francois Lecomte, Pablo Alvarez, Stéphane Cotin, Jean-Louis Dillenseger. Beyond respiratory models: a physics-enhanced synthetic data generation method for 2D-3D deformable registration. CVPR DCAMI Workshop, Jun 2024, Seattle (USA), Washington, United States. ⟨hal-04599413⟩
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