maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images

TL;DR

maskSLIC is a novel extension of the SLIC supervoxel method designed to generate supervoxels within irregular regions of interest, improving tumor subregion representation and outperforming traditional SLIC in brain tumor analysis.

Contribution

We introduce maskSLIC, a new supervoxel method that effectively handles irregular masks, enhancing tumor subregion analysis in medical images.

Findings

maskSLIC outperforms SLIC on brain tumor data (p=0.001).

Achieves better tumor subregion representation.

More effective than voxel clustering for functional tumor analysis.

Abstract

Supervoxel methods such as Simple Linear Iterative Clustering (SLIC) are an effective technique for partitioning an image or volume into locally similar regions, and are a common building block for the development of detection, segmentation and analysis methods. We introduce maskSLIC an extension of SLIC to create supervoxels within regions-of-interest, and demonstrate,on examples from 2-dimensions to 4-dimensions, that maskSLIC overcomes issues that affect SLIC within an irregular mask. We highlight the benefits of this method through examples, and show that it is able to better represent underlying tumour subregions and achieves significantly better results than SLIC on the BRATS 2013 brain tumour challenge data (p=0.001) - outperforming SLIC on 18/20 scans. Finally, we show an application of this method for the analysis of functional tumour subregions and demonstrate that it is more effective than voxel clustering.