Votenet++: Registration Refinement For Multi-Atlas Segmentation

Abstract

Multi-atlas segmentation (MAS) is a popular image segmen- tation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors be- fore label fusion. Specifically, we use a volumetric displace- ment 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.

Publication
18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, France, April 13-16, 2021
Zhipeng Ding
Zhipeng Ding
Graduate Student in Computer Science

My research interests include image registration and machine learning.

Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.

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