Femoral cartilage segmentation in knee MRI scans using two stage voxel classification

TY - GEN

T1 - Femoral cartilage segmentation in knee MRI scans using two stage voxel classification

AU - Prasoon, Adhish

AU - Igel, Christian

AU - Loog, Marco

AU - Lauze, Francois Bernard

AU - Dam, Erik

AU - Nielsen, Mads

N1 - Conference code: 35

PY - 2013

Y1 - 2013

N2 - Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines (SVMs). Here we apply it to femoral cartilage segmentation. We describe the similarities and differences between segmenting these two knee cartilages. For further speeding up batch SVM training, we propose loosening the stopping condition in the quadratic program solver before considering moving on to other approximation techniques such as online SVMs. The two-stage approach reached a higher accuracy in comparison to the one-stage state-of-the-art method. It also achieved better inter-scan segmentation reproducibility when compared to a radiologist as well as the current state-of-the-art method.

AB - Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines (SVMs). Here we apply it to femoral cartilage segmentation. We describe the similarities and differences between segmenting these two knee cartilages. For further speeding up batch SVM training, we propose loosening the stopping condition in the quadratic program solver before considering moving on to other approximation techniques such as online SVMs. The two-stage approach reached a higher accuracy in comparison to the one-stage state-of-the-art method. It also achieved better inter-scan segmentation reproducibility when compared to a radiologist as well as the current state-of-the-art method.

U2 - 10.1109/EMBC.2013.6610787

DO - 10.1109/EMBC.2013.6610787

M3 - Article in proceedings

SP - 5469

EP - 5472

BT - 35th Annual International Conference of the IEEE; Engineering in Medicine and Biology Society (EMBC), 2013

PB - IEEE

T2 - Annual International Conference of the IEEE 2013

Y2 - 3 July 2013 through 7 July 2013

ER -