2019 JRNL PP Shumao Pang - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
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 toembedded featuremanifold,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-theart
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.