IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration
This addresses the need for real-time organ shape estimation in image-guided medical procedures, though it appears incremental as it extends existing methods to new organs.
The paper tackles the problem of reconstructing 3D organ shapes from a single 2D projection image for clinical applications like radiotherapy, achieving deformable registration with clinically acceptable accuracy for multiple abdominal organs.
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.