White Matter Segmentation Algorithm for DTI Images Based on Super-Pixel Full Convolutional Network.

Abstract:

:Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the existing deep learning model, an improved image semantic segmentation method based on super-pixels and conditional random field is proposed. Firstly, this paper uses the existing feature extraction model based on deep learning to obtain rough semantic segmentation results, including high-level semantic information of the image but lacking details of the image. In addition, the super-pixel segmentation algorithm is implemented to obtain super-pixels that carries more low-level information. Secondly, due to the lack of image details in rough segmentation results, the segmentation of the edge of the image is inaccurate. In this paper, a boundary optimization algorithm is proposed to optimize the edge segmentation accuracy of the rough results. Finally, the use of super-pixels for local boundary optimization can improve the segmentation accuracy. Experiments results show that this segment is a practical and effective method.

journal_name

J Med Syst

authors

Mu Y,Li Q,Zhang Y

doi

10.1007/s10916-019-1431-1

subject

Has Abstract

pub_date

2019-08-12 00:00:00

pages

303

issue

9

eissn

0148-5598

issn

1573-689X

pii

10.1007/s10916-019-1431-1

journal_volume

43

pub_type

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