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

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>

T2 - International Conference in Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Y2 - 29 November 2010

ER -