Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation

TL;DR

This paper introduces a new multi-atlas hippocampus segmentation method that combines semi-supervised label propagation with random forests, leveraging local and global image features for improved accuracy.

Contribution

The novel integration of semi-supervised label propagation and random forests within a label fusion framework enhances hippocampus segmentation performance in MR images.

Findings

Achieved superior segmentation accuracy compared to state-of-the-art methods.

Effectively utilized local and global image appearance for label propagation.

Demonstrated robustness across different MR image datasets.

Abstract

A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.