VoteNet++: Registration Refinement for Multi-Atlas Segmentation

TL;DR

This paper introduces VoteNet++, a method that refines registration in multi-atlas segmentation using a volumetric displacement field, leading to improved accuracy in medical image segmentation.

Contribution

It proposes a novel registration refinement technique that leverages anatomical appearance and label predictions to enhance multi-atlas segmentation performance.

Findings

Refinement improves segmentation accuracy on knee MRI data.

Using label information enhances registration quality.

Initial alignment significantly affects MAS performance.

Abstract

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.