Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme

Jenny Maria Folkesson; Erik Bjørnager Dam; Ole Fogh Olsen; Paola C. Pettersen; Claus Christiansen

doi:10.1007/11566465

Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme

Jenny Maria Folkesson, Erik Bjørnager Dam, Ole Fogh Olsen, Paola C. Pettersen, Claus Christiansen

35 Citations (Scopus)

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.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention – MICCAI
Publisher	<Forlag uden navn>
Publication date	2005
Pages	327-334
ISBN (Print)	978-3-540-29327-9
DOIs	https://doi.org/10.1007/11566465
Publication status	Published - 2005
Externally published	Yes
Event	8th International Conference in Medical Image Computing and Computer-Assisted Intervention – MICCAI - Palm Springs, CA, United States Duration: 29 Nov 2010 → … Conference number: 8

Conference

Conference	8th International Conference in Medical Image Computing and Computer-Assisted Intervention – MICCAI
Number	8
Country/Territory	United States
City	Palm Springs, CA
Period	29/11/2010 → …

Series	Lecture notes in computer science
Volume	3749/2005
ISSN	0302-9743

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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 et al.
Medical Image Computing and Computer-Assisted Intervention – MICCAI. <Forlag uden navn>, 2005. p. 327-334 (Lecture notes in computer science, Vol. 3749/2005).

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

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. in Medical Image Computing and Computer-Assisted Intervention – MICCAI. <Forlag uden navn>, Lecture notes in computer science, vol. 3749/2005, pp. 327-334, 8th International Conference in Medical Image Computing and Computer-Assisted Intervention – MICCAI, Palm Springs, CA, United States, 29/11/2010. https://doi.org/10.1007/11566465

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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.",

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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.

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ER -

Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme

Abstract

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