MATLAB PROJECT
Hippocampus
Segmentation Based on Iterative Local Linear Mapping With Representative and
Local Structure-Preserved Feature Embedding
Abstract:
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 the representative and local structure-preserved
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
structure-preserved 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. Subsequently, threshold segmentation generates a
preliminary segmentation. The 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. The experiments on 135
subjects from ADNI dataset show that the proposed approach is superior to the
state-of-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.
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