Fully Automatic and Highly Accurate Segmentation of the Proximal Femur in AP Pelvic Radiographs |
Our fully automatic segmentation tool outperforms alternative matching techniques significantly when starting searching from the mean shape at true pose. It achieves a mean point-to-curve error of less than 0.9mm for 99% of all images.
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We tested our fully automatic proximal femur segmentation system on 839 AP pelvic radiographs (527 females, 312 males) using two-fold cross-validation experiments. The aim was to place 65 dense points along the front-view contour of the proximal femur. We report averaged mean point-to-curve errors as a percentage of the shaft width (based on a subset of calibrated images we estimated the latter to be 37mm). |
Our femur segmentation system is fully automatic and highly accurate. It achieves a mean point-to-curve error of less than 0.9mm for 99% of all images. This is equally good as a local search started from the mean shape at true pose (see left plot).
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Examples of segmentation results of the fully automated system (sorted by mean point-to-curve error percentiles): a) median – 0.4mm; b) 91.2% – 0.6mm, highest global search error (ie. the global search had a success rate of 100%); c) 99.0% – 0.9mm; d) maximal overall error – 2.7mm. |
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