Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification

Mohammad Havaei, Pierre-Marc Jodoin, Hugo Larochelle

Abstract: We consider the problem of brain tumor segmentation from magnetic resonance (MR) images. This task is most frequently tackled using machine learning methods that generalize across brains, by learning from training brain images in order to generalize to novel test brains. However this approach faces many obstacles that threaten its performance, such as the ability to properly perform multi-brain registration or brain-atlas alignment, or to extract appropriate high-dimensional features that support good generalization. These operations are both nontrivial and time-consuming, limiting the practicality of these approaches in a clinical context. In this paper, we propose to side step these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a given brain by training and generalizing within that brain only, based on some minimum user interaction. We investigate how k nearest neighbors (kNN), arguably the simplest machine learning method available, combined with the simplest feature vector possible (raw MR signal + (x,y,z) position) can be combined into a method that is both simple, accurate and fast. Results obtained on the online BRATS dataset reveal that our method is fast and second best in terms of the complete and core test set tumor segmentation.