WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network
This work addresses medical image registration for healthcare applications, but it is incremental as it builds on existing weakly supervised and attention-based techniques.
The authors tackled medical image registration by introducing WSSAMNet, a weakly supervised two-step method that uses segmentation masks to attend to input volumes before registration, achieving competitive performance against ANTs and VoxelMorph on the BraTSReg challenge data.
We present WSSAMNet, a weakly supervised method for medical image registration. Ours is a two step method, with the first step being the computation of segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the moving volumes. We study the effectiveness of our technique on the BraTSReg challenge data against ANTs and VoxelMorph, where we demonstrate that our method performs competitively.