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

Imagine you're a surgeon preparing for a complex aortic surgery, where understanding the anatomy of blood vessels is crucial. Current deep learning models can sometimes produce results that look plausible numerically but don't align with real anatomical structures, leading to potential misguidance during procedures. This is particularly problematic when data is limited, as the models struggle to generalize accurately, which is what's called overfitting. The challenge is to create a model that not only predicts outcomes but also respects the inherent anatomical constraints of the human body.

To address this, the authors propose a new approach called Anatomy-Informed Neural Networks (AINN). This method incorporates soft and hard anatomical priors directly into the model's architecture and loss function. For instance, it penalizes unlikely anatomical configurations, like a renal artery branching from the aorta, making such predictions less likely. The model is tested on a clinical scenario involving the deformation of the aortoiliac tree when a stiff wire is introduced, using a sophisticated mathematical framework that connects the predicted outcomes to real-world angiograms. This approach allows for training a 3D model using 2D images, which could significantly enhance predictive accuracy while reducing the amount of training data needed. Compared to previous methods, this model not only aims to improve predictions but also ensures that invalid anatomical configurations are avoided by design.

The integration of anatomical priors into neural networks represents a significant methodological advancement.

The approach is validated against known ground truth, though further testing is needed.