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

Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images