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Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme. / Folkesson, Jenny Maria; Dam, Erik Bjørnager; Olsen, Ole Fogh; Pettersen, Paola C.; Christiansen, Claus.
Medical Image Computing and Computer-Assisted Intervention – MICCAI. <Forlag uden navn>, 2005. s. 327-334 (Lecture notes in computer science, Bind 3749/2005).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Folkesson, JM
, Dam, EB, Olsen, OF, Pettersen, PC & Christiansen, C 2005,
Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme. i
Medical Image Computing and Computer-Assisted Intervention – MICCAI. <Forlag uden navn>, Lecture notes in computer science, bind 3749/2005, s. 327-334, 8th International Conference in Medical Image Computing and Computer-Assisted Intervention – MICCAI, Palm Springs, CA, USA,
29/11/2010.
https://doi.org/10.1007/11566465APA
Folkesson, J. M.
, Dam, E. B., Olsen, O. F., Pettersen, P. C., & Christiansen, C. (2005).
Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme. I
Medical Image Computing and Computer-Assisted Intervention – MICCAI (s. 327-334). <Forlag uden navn>. Lecture notes in computer science Bind 3749/2005
https://doi.org/10.1007/11566465Vancouver
Folkesson JM
, Dam EB, Olsen OF, Pettersen PC, Christiansen C.
Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme. I Medical Image Computing and Computer-Assisted Intervention – MICCAI. <Forlag uden navn>. 2005. s. 327-334. (Lecture notes in computer science, Bind 3749/2005).
https://doi.org/10.1007/11566465Author
Folkesson, Jenny Maria ; Dam, Erik Bjørnager ; Olsen, Ole Fogh ; Pettersen, Paola C. ; Christiansen, Claus. / Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme. Medical Image Computing and Computer-Assisted Intervention – MICCAI. <Forlag uden navn>, 2005. s. 327-334 (Lecture notes in computer science, Bind 3749/2005).
Bibtex
@inproceedings{2bbd92304c3311dd8d9f000ea68e967b,
title = "Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme",
abstract = "Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.",
author = "Folkesson, {Jenny Maria} and Dam, {Erik Bj{\o}rnager} and Olsen, {Ole Fogh} and Pettersen, {Paola C.} and Claus Christiansen",
year = "2005",
doi = "10.1007/11566465",
language = "English",
isbn = "978-3-540-29327-9",
series = "Lecture notes in computer science",
publisher = "<Forlag uden navn>",
pages = "327--334",
booktitle = "Medical Image Computing and Computer-Assisted Intervention – MICCAI",
note = "null ; Conference date: 29-11-2010",
}
RIS
TY - GEN
T1 - Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme
AU - Folkesson, Jenny Maria
AU - Dam, Erik Bjørnager
AU - Olsen, Ole Fogh
AU - Pettersen, Paola C.
AU - Christiansen, Claus
N1 - Conference code: 8
PY - 2005
Y1 - 2005
N2 - Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.
AB - Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.
U2 - 10.1007/11566465
DO - 10.1007/11566465
M3 - Article in proceedings
SN - 978-3-540-29327-9
T3 - Lecture notes in computer science
SP - 327
EP - 334
BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI
PB - <Forlag uden navn>
Y2 - 29 November 2010
ER -
