  Training machine learning models to segment tumors and other anomalies in
medical images is an important step for developing diagnostic tools but
generally requires manually annotated ground truth segmentations, which
necessitates significant time and resources. This work proposes the use of a
superpixel generation model and a superpixel clustering model to enable weakly
supervised brain tumor segmentations. The proposed method utilizes binary
image-level classification labels, which are readily accessible, to
significantly improve the initial region of interest segmentations generated by
standard weakly supervised methods without requiring ground truth annotations.
We used 2D slices of magnetic resonance brain scans from the Multimodal Brain
Tumor Segmentation Challenge 2020 dataset and labels indicating the presence of
tumors to train the pipeline. On the test cohort, our method achieved a mean
Dice coefficient of 0.691 and a mean 95% Hausdorff distance of 18.1,
outperforming existing superpixel-based weakly supervised segmentation methods.
