VoteNet++: Registration Refinement for Multi-Atlas Segmentation

10/26/2020 ∙ by Zhipeng Ding, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.