NDLI: Automatic kidney segmentation in CT images based on multi-atlas image registration

Content Provider	IEEE Xplore Digital Library
Author	Guanyu Yang Jinjin Gu Yang Chen Wangyan Liu Lijun Tang Huazhong Shu Toumoulin, C.
Copyright Year	2014
Description	Author affiliation: LTSI, Univ. de Rennes 1, Rennes, France (Toumoulin, C.) \|\| Minist. of Educ., Southeast Univ., Nanjing, China (Guanyu Yang; Jinjin Gu; Yang Chen; Huazhong Shu) \|\| Dept. of Radiol., First Affiliated Hosp. of Nanjing Med. Univ., Nanjing, China (Wangyan Liu; Lijun Tang)
Abstract	Kidney segmentation is an important step for computer-aided diagnosis or treatment in urology. In this paper, we present an automatic method based on multi-atlas image registration for kidney segmentation. The method mainly relies on a two-step framework to obtain coarse-to-fine segmentation results. In the first step, down-sampled patient image is registered with a set of low-resolution atlas images. A coarse kidney segmentation result is generated to locate the left and right kidneys. In the second step, the left and right kidneys are cropped from original images and aligned with another set of high-resolution atlas images to obtain the final results respectively. Segmentation results from 14 CT angiographic (CTA) images show that our proposed method can segment the kidneys with a high accuracy. The average Dice similarity coefficient and surface-to-surface distance between segmentation results and reference standard are 0.952 and 0.913mm. Furthermore, the kidney segmentation in CT urography (CTU) and CTA images of 12 patients were performed to show the feasibility of our method in CTU images.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	5538
Ending Page	5541
File Size	1961895
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944881
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kidney Image segmentation Computed tomography Image registration Three-dimensional displays Accuracy Standards
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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