21 March 2016 Breast segmentation in MRI using Poisson surface reconstruction initialized with random forest edge detection
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Abstract
Segmentation of breast tissue in MRI images is an important pre-processing step for many applications. We present a new method that uses a random forest classifier to identify candidate edges in the image and then applies a Poisson reconstruction step to define a 3D surface based on the detected edge points. Using a leave one patient out cross validation we achieve a Dice overlap score of 0.96 ± 0.02 for T1 weighted non-fat suppressed images in 8 patients. In a second dataset of 332 images acquired using a Dixon sequence, which was not used in training the random classifier, the mean Dice score was 0.90 ± 0.03. Using this approach we have achieved accurate, robust segmentation results using a very small training set.
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Anne L. Martel, Anne L. Martel, Cristina Gallego-Ortiz, Cristina Gallego-Ortiz, YingLi Lu, YingLi Lu, "Breast segmentation in MRI using Poisson surface reconstruction initialized with random forest edge detection", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841B (21 March 2016); doi: 10.1117/12.2214416; https://doi.org/10.1117/12.2214416
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