Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images
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Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images. / de Bruijne, Marleen; van Ginneken, Bram; Bartels, Lambertus W.; van der Laan, Maarten J.; Blankensteijn, Jan D.; Niessen, Wiro J.; Viergever, Max. A.
Medical Image Computing and Computer-Assisted Intervention - MICCAI. <Forlag uden navn>, 2003. p. 538-545 (Lecture notes in computer science, Vol. 2879/2003).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images
AU - de Bruijne, Marleen
AU - van Ginneken, Bram
AU - Bartels, Lambertus W.
AU - van der Laan, Maarten J.
AU - Blankensteijn, Jan D.
AU - Niessen, Wiro J.
AU - Viergever, Max. A.
N1 - Conference code: 6
PY - 2003
Y1 - 2003
N2 - An automated method for segmenting the outer boundary of abdominal aortic aneurysms in MR images is presented. The method is based on the well known Active Shape Models (ASM), which fit a global landmark-based shape model on the basis of local boundary appearance models. The original three-dimensional ASM scheme is modified to deal with multi-spectral image information and inconsistent boundary appearance in a principled way, with only a limited amount of training data. In addition, a framework for user interaction is proposed. If required, the obtained segmentation can be corrected in an interactive manner by indicating points on the desired boundary. The methods are evaluated in leave-one-out experiments on 21 datasets. A segmentation scheme combining gray level information from two or three MR sequences produces significantly better results than a single-scan model. Average volume errors with respect to the manual segmentation are 4.0%, in 19 out of 21 datasets. In the cases in which the obtained error is large, results can easily be improved using the interactive scheme.
AB - An automated method for segmenting the outer boundary of abdominal aortic aneurysms in MR images is presented. The method is based on the well known Active Shape Models (ASM), which fit a global landmark-based shape model on the basis of local boundary appearance models. The original three-dimensional ASM scheme is modified to deal with multi-spectral image information and inconsistent boundary appearance in a principled way, with only a limited amount of training data. In addition, a framework for user interaction is proposed. If required, the obtained segmentation can be corrected in an interactive manner by indicating points on the desired boundary. The methods are evaluated in leave-one-out experiments on 21 datasets. A segmentation scheme combining gray level information from two or three MR sequences produces significantly better results than a single-scan model. Average volume errors with respect to the manual segmentation are 4.0%, in 19 out of 21 datasets. In the cases in which the obtained error is large, results can easily be improved using the interactive scheme.
U2 - 10.1007/b93811
DO - 10.1007/b93811
M3 - Article in proceedings
SN - 978-3-540-20464-0
T3 - Lecture notes in computer science
SP - 538
EP - 545
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI
PB - <Forlag uden navn>
Y2 - 29 November 2010
ER -
ID: 5555723