Within-Brain Classification for Brain Tumor Segmentation

TL;DR

This paper introduces a within-brain classification framework for interactive brain tumor segmentation that improves accuracy and efficiency by training models specific to each brain, avoiding MRI-specific noise issues.

Contribution

It proposes a novel within-brain generalization approach for tumor segmentation, enhancing accuracy and efficiency over traditional cross-brain methods.

Findings

Adding spatial features improves classification performance.

Using tailored kernels and hyper-parameters enhances results.

Achieves second-best accuracy on MICCAI-BRATS 2013 dataset.

Abstract

Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. Methods: This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. Conclusion: We investigate how adding spatial feature coordinates (i.e. $i$, $j$, $k$) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. Results: As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.