Breast segmentation in MRI using Poisson surface reconstruction initialized with random forest edge detection

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.