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

Folkesson, Jenny; Dam, Erik B.; Olsen, Ole; Pettersen, P.C.; Christiansen, Claus

doi:10.1007/11566465_41

Cited by 37 publications

(27 citation statements)

References 16 publications

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“…We combine one binary classifier trained to separate tibial cartilage from the rest and one trained to separate femoral cartilage from the rest with a rejection threshold [27], [28]. The outcome of a one vs. rest classifier can be seen as the posterior probabilities that, for all the voxels in the image, a voxel with feature vector belongs to class , where is the number of classes.…”

Section: B Multiclass Classification By Combining Binary Classifiersmentioning

confidence: 99%

Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach

Folkesson

Dam

Olsen

et al. 2007

IEEE Trans. Med. Imaging

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Abstract-We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.

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Section: B Multiclass Classification By Combining Binary Classifiersmentioning

confidence: 99%

Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach

Folkesson

Dam

Olsen

et al. 2007

IEEE Trans. Med. Imaging

Self Cite

166

159

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show abstract

“…To make a quantitative comparison between manual segmentation and automatic segmentation, the DSC was calculated, which is the gold standard for evaluating segmentation results, the details of which are discussed in [14].…”

Section: Resultsmentioning

confidence: 99%

Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images

Pang

Qiu

et al. 2015

J Digit Imaging

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An automatic method for cartilage segmentation using knee MRI images is described. Three binary classifiers with integral and partial pixel features are built using the Bayesian theorem to segment the femoral cartilage, tibial cartilage and patellar cartilage separately. First, an iterative procedure based on the feedback of the number of strong edges is designed to obtain an appropriate threshold for the Canny operator and to extract the bone-cartilage interface from MRI images. Second, the different edges are identified based on certain features, which allow for different cartilage to be distinguished synchronously. The cartilage is segmented preliminarily with minimum error Bayesian classifiers that have been previously trained. According to the cartilage edge and its anatomic location, the speed of segmentation is improved. Finally, morphological operations are used to improve the primary segmentation results. The cartilage edge is smooth in the automatic segmentation results and shows good consistency with manual segmentation results. The mean Dice similarity coefficient is 0.761.

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“…Assuming that most of the errors occur at the boundaries, small object such as patella cartilage are penalized and get lower DSC score than larger objects such as the tibial or femoral cartilage. Still, our approach obtains better results than the automatic classification scheme proposed in [6]. From [17] we consider only the results of the proposed Patch-based Active Appearance Model, since it performs best in their evaluation of different automatic model-based approaches.…”

Section: Methodsmentioning

confidence: 99%

“…Folkesson et al [6] propose a hierarchical classification scheme for automatic segmentation of cartilage in low field MR images. A semi-automatic method based on watershed transformation and pre-segmentation using [6] is presented by Dam et al. [7].…”

Section: Introductionmentioning

confidence: 99%

Primal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation

Glocker

Komodakis

Paragios

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.

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

Cited by 37 publications

References 16 publications

Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach

Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach

Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images

Primal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation

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