VoteNet++: Registration Refinement for Multi-Atlas Segmentation
This work addresses segmentation accuracy for medical imaging applications, but it is incremental as it builds on existing multi-atlas segmentation methods.
The paper tackled the problem of registration errors in multi-atlas segmentation for medical images by refining registrations using a volumetric displacement field based on anatomical appearance and predicted labels, resulting in improved performance on a 3D MRI knee dataset.
Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors before label fusion. Specifically, we use a volumetric displacement field to refine registrations based on image anatomical appearance and predicted labels. We show the influence of the initial spatial alignment as well as the beneficial effect of using label information for MAS performance. Experiments demonstrate that the proposed refinement approach improves MAS performance on a 3D magnetic resonance dataset of the knee.