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.
SPIE Medical Imaging