Hippocampus Segmentation based on Iterative Local Linear Mapping with Representative and Local Structure_preserved Feature Embedding
Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer‚??s disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approach needs high computation cost due to registration and the segmentation accuracy is subject to the registration accuracy. In this paper, we propose a novel method based on iterative local linear mapping (ILLM) with representative and local structurepreserved feature embedding to achieve accurate and robust hippocampus segmentation with no need for registration. In the proposed approach, semi-supervised deep autoencoder (SSDA) exploits unsupervised deep autoencoder and local structurepreserved manifold regularization to nonlinearly transform the extracted magnetic resonance (MR) patch to embedded feature manifold, whose adjacent relationship is similar to the signed distance map (SDM) patch manifold. Local linear mapping is used to preliminarily predict SDM patch corresponding to the MR patch. Then a threshold segmentation generates a preliminary segmentation. ILLM refines the segmentation result iteratively by ensuring the local constraints of embedded feature manifold and SDM patch manifold using a space-constrained dictionary update. Thus a refined segmentation is obtained with no need for registration. Experiments on 135 subjects from ADNI dataset show that the proposed approach is superior to the stateof- the-art PBMAS and classification-based approaches with mean Dice similarity coefficients of 0.8852 0.0203 and 0.8783 0.0251 for bilateral hippocampus segmentation of 1.5T and 3.0T datasets respectively.
Deep learning, Multi-atlas segmentation, Iterative local linear mapping, Manifold regularization