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Proceedings Paper

A fully automatic multi-atlas based segmentation method for prostate MR images
Author(s): Zhiqiang Tian; LiZhi Liu; Baowei Fei
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Paper Abstract

Most of multi-atlas segmentation methods focus on the registration between the full-size volumes of the data set. Although the transformations obtained from these registrations may be accurate for the global field of view of the images, they may not be accurate for the local prostate region. This is because different magnetic resonance (MR) images have different fields of view and may have large anatomical variability around the prostate. To overcome this limitation, we proposed a two-stage prostate segmentation method based on a fully automatic multi-atlas framework, which includes the detection stage i.e. locating the prostate, and the segmentation stage i.e. extracting the prostate. The purpose of the first stage is to find a cuboid that contains the whole prostate as small cubage as possible. In this paper, the cuboid including the prostate is detected by registering atlas edge volumes to the target volume while an edge detection algorithm is applied to every slice in the volumes. At the second stage, the proposed method focuses on the registration in the region of the prostate vicinity, which can improve the accuracy of the prostate segmentation. We evaluated the proposed method on 12 patient MR volumes by performing a leave-one-out study. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are used to quantify the difference between our method and the manual ground truth. The proposed method yielded a DSC of 83.4%±4.3%, and a HD of 9.3 mm±2.6 mm. The fully automated segmentation method can provide a useful tool in many prostate imaging applications.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941340 (20 March 2015); doi: 10.1117/12.2082229
Show Author Affiliations
Zhiqiang Tian, Emory Univ. (United States)
LiZhi Liu, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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