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Intensity modulated radiotherapy (IMRT) is one of the most common modalitiesfor treating cancer patients. One of the biggest challenges is precisetreatment delivery that accounts for varying motion patterns originating fromfree-breathing. Currently, image-guided solutions for IMRT is limited to 2Dguidance due to the complexity of 3D tracking solutions. We propose a novelend-to-end attention graph neural network model that generates in real-time atriangular shape of the liver based on a reference segmentation obtained at thepreoperative phase and a 2D MRI coronal slice taken during the treatment. Graphneural networks work directly with graph data and can capture hidden patternsin non-Euclidean domains. Furthermore, contrary to existing methods, itproduces the shape entirely in a mesh structure and correctly infers mesh shapeand position based on a surrogate image. We define two on-the-fly approaches tomake the correspondence of liver mesh vertices with 2D images obtained duringtreatment. Furthermore, we introduce a novel task-specific identity loss toconstrain the deformation of the liver in the graph neural network to limitphenomenons such as flying vertices or mesh holes. The proposed method achievesresults with an average error of 3.06 +- 0.7 mm and Chamfer distance with L2norm of 63.14 +- 27.28.

End-to-end Deformable Attention Graph Neural Network for Single-view Liver Mesh Reconstruction