Abstract
Active Shape Model (ASM) has been successfully applied in the segmentation of Diffusion Tensor Magnetic Resonance Image (DT-MRI, referred to as DTI) of brain. However, due to multiple anatomical structure types, irregular shapes, small gray-scale and large amount of these images, perfect segmentation performance could not be achieved. Especially, it is sensitive to initial values with high computational complexity. In this paper, we introduce the gray information of multiple atlases and the prior information of target shapes into the ASM and propose the Multi-Atlas Active Shape Model (referred to as MA-ASM) approach for DTI segmentation. It was evaluated in a manually labeled database with 7 Region of Interest (ROI)s for each of 20 subjects. In comparison with the state of art method of STAPLE (Simultaneous Truth Performance Level Estimation), the proposed algorithm was closer to the manual segmentation shape by subjective visual effects, and had higher overlap rates and lower error detection rates on quantitative analysis than STAPLE.
Highlights
The medical image segmentation is an important field for diagnosing and analyzing neurological and mental diseases, which are usually related to the abnormal fiber bundles of brain White Matter (WM)
Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, referred to as DTI) is a new Magnetic Resonance Imaging (MRI) technology, which can obtain the information of tissue fiber structures by measuring the different diffusion of water molecules caused by different tissue structures in body [35]
The results clearly show that the segmentation results of MA-Active Shape Model (ASM) based algorithm are better than Simultaneous Truth Performance Level Estimation (STAPLE) based algorithm
Summary
The medical image segmentation is an important field for diagnosing and analyzing neurological and mental diseases, which are usually related to the abnormal fiber bundles of brain White Matter (WM). Segmentation of WM fiber bundle in DTI image plays a vital role for the diagnosis. All DTI segmentation algorithms mainly belong to three categories: manual segmentation, segmentation with prior knowledge of image, and segmentation without prior knowledge of image, such as the similarity and topological consistency of the same tissues among different individuals. Manual segmentation method is the gold standard for medical image segmentation. It takes more time, and extremely depends on the experts’ experience and subjectivity, and the process has no repeat ability [27]
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