Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

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

Harvard

de Bruijne, M, van Ginneken, B, Bartels, LW, van der Laan, MJ, Blankensteijn, JD, Niessen, WJ & Viergever, MA 2003, Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images. in Medical Image Computing and Computer-Assisted Intervention - MICCAI. <Forlag uden navn>, Lecture notes in computer science, vol. 2879/2003, pp. 538-545, International Conference in Medical Image Computing and Computer-Assisted Intervention (MICCAI), Montreal, Canada, 29/11/2010. https://doi.org/10.1007/b93811

APA

de Bruijne, M., van Ginneken, B., Bartels, L. W., van der Laan, M. J., Blankensteijn, J. D., Niessen, W. J., & Viergever, M. A. (2003). Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images. In Medical Image Computing and Computer-Assisted Intervention - MICCAI (pp. 538-545). <Forlag uden navn>. Lecture notes in computer science Vol. 2879/2003 https://doi.org/10.1007/b93811

Vancouver

de Bruijne M, van Ginneken B, Bartels LW, van der Laan MJ, Blankensteijn JD, Niessen WJ et al. Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images. In Medical Image Computing and Computer-Assisted Intervention - MICCAI. <Forlag uden navn>. 2003. p. 538-545. (Lecture notes in computer science, Vol. 2879/2003). https://doi.org/10.1007/b93811

Author

de Bruijne, Marleen ; van Ginneken, Bram ; Bartels, Lambertus W. ; van der Laan, Maarten J. ; Blankensteijn, Jan D. ; Niessen, Wiro J. ; Viergever, Max. A. / Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images. Medical Image Computing and Computer-Assisted Intervention - MICCAI. <Forlag uden navn>, 2003. pp. 538-545 (Lecture notes in computer science, Vol. 2879/2003).

Bibtex

@inproceedings{ea96e8606d0711dd8d9f000ea68e967b,
title = "Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images",
abstract = "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. ",
author = "{de Bruijne}, Marleen and {van Ginneken}, Bram and Bartels, {Lambertus W.} and {van der Laan}, {Maarten J.} and Blankensteijn, {Jan D.} and Niessen, {Wiro J.} and Viergever, {Max. A.}",
year = "2003",
doi = "10.1007/b93811",
language = "English",
isbn = "978-3-540-20464-0",
series = "Lecture notes in computer science",
publisher = "<Forlag uden navn>",
pages = "538--545",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI",
note = "null ; Conference date: 29-11-2010",

}

RIS

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