Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images
This work addresses the need for efficient diagnostic tools in medical imaging by reducing reliance on costly manual annotations, though it is incremental as it builds on existing weakly supervised methods.
The paper tackles the problem of segmenting brain tumors in MR images without requiring manual ground truth annotations by using superpixel generation and clustering with binary image-level labels. It achieved a mean Dice coefficient of 0.691 and a mean 95% Hausdorff distance of 18.1, outperforming existing superpixel-based weakly supervised methods.
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.