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

arXiv: https://arxiv.org/abs/2209.09930 · HTML: https://mlsanity.com/paper/2209.09930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation](https://mlsanity.com/paper/1603.06098/) (2016)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [Augmenting Convolutional networks with attention-based aggregation](https://mlsanity.com/paper/2112.13692/) (2021)
- [Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection](https://mlsanity.com/paper/1905.13456/) (2019)
- [Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules](https://mlsanity.com/paper/1707.01086/) (2017)
- [Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution](https://mlsanity.com/paper/2011.01045/) (2020)
- [Modality-Pairing Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2010.09277/) (2020)
- [Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2004.02009/) (2020)
- [H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task](https://mlsanity.com/paper/2012.15318/) (2020)
- [Automatic Brain Tumor Segmentation with Scale Attention Network](https://mlsanity.com/paper/2011.03188/) (2020)
- [A Transfer Learning Based Active Learning Framework for Brain Tumor Classification](https://mlsanity.com/paper/2011.09265/) (2020)
- [AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation](https://mlsanity.com/paper/2101.10696/) (2021)
- [Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning](https://mlsanity.com/paper/2111.14959/) (2021)
- [Cardiac Adipose Tissue Segmentation via Image-Level Annotations](https://mlsanity.com/paper/2206.04238/) (2022)

## Built on by (most cited first, 1 of 1)

- [Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images](https://mlsanity.com/paper/2211.05269/) (2022)

## Abstract (arXiv)

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.