Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach

Abstract

Supervised classification techniques are among the most powerful methods used for automatic segmentation of medical images. A disadvantage of these methods is that they require a representative training set and thus encounter problems when the training data is acquired e.g. with a different scanner protocol than the target segmentation data. We therefore propose a framework for supervised biomedical image segmentation across different scanner protocols, by means of transfer learning. We establish a transfer learning algorithm for classification, which can exploit a large amount of labeled samples from different sources in addition to a small amount of samples from the target source. The algorithm iteratively re-weights the contribution of training samples from these different sources based on classification by a weighted SVM classifier. We evaluate this technique by performing tissue classification on MRI brain data from four substantially different scanning protocols. For a small number of labeled samples from a single image obtained with the same protocol, the proposed transfer learning method outperforms classification on all available training data as well as classification based on the labeled target samples only. The classification errors for these cases can be reduced with up to 40 percent compared to traditional classification techniques.