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arXiv AI By David P. Stonko

Anatomy-Informed Neural Networks: Encoding Anatomic Priors in Loss and Architecture, with an SE(3) Formulation of Guidewire-Induced Aortoiliac Deformation

Read the original on arXiv AI →

The paper introduces Anatomy-Informed Neural Networks (AINN), which embed soft and hard anatomical priors into the loss function and network architecture to prevent anatomically impossible predictions. AINN is applied to a clinical scenario of aortoiliac deformation caused by a guidewire, modeling vessel and wire dynamics in SE(3) and training with a Wasserstein-2 loss from 2D angiograms. The study verifies the kinematics and loss against ground truth but does not yet train a network, outlining future work to apply the model to real CT data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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