# Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

arXiv: https://arxiv.org/abs/1802.10508 · HTML: https://mlsanity.com/paper/1802.10508/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 30 of 56)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
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- [Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2104.06231/) (2021)
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- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [Cross-Modality Deep Feature Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2201.02356/) (2022)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2303.07126/) (2023)
- [Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities](https://mlsanity.com/paper/2111.04735/) (2021)
- [Label fusion and training methods for reliable representation of inter-rater uncertainty](https://mlsanity.com/paper/2202.07550/) (2022)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
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- [A Tri-attention Fusion Guided Multi-modal Segmentation Network](https://mlsanity.com/paper/2111.01623/) (2021)
- [Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images](https://mlsanity.com/paper/2208.03934/) (2022)
- [CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation](https://mlsanity.com/paper/2109.05443/) (2021)
- [Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction](https://mlsanity.com/paper/2104.01149/) (2021)
- [Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data](https://mlsanity.com/paper/2112.01034/) (2021)
- [Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation](https://mlsanity.com/paper/2105.14250/) (2021)
- [Application of belief functions to medical image segmentation: A review](https://mlsanity.com/paper/2205.01733/) (2022)
- [3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint](https://mlsanity.com/paper/2102.03111/) (2021)
- [Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2302.02521/) (2023)
- [AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2404.00122/) (2024)
- [Glioblastoma Multiforme Patient Survival Prediction](https://mlsanity.com/paper/2101.10589/) (2021)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [Brain tumour segmentation using a triplanar ensemble of U-Nets](https://mlsanity.com/paper/2105.11356/) (2021)
- [Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2110.15884/) (2021)
- [A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction](https://mlsanity.com/paper/2204.01201/) (2022)
- [A Novel Collaborative Self-Supervised Learning Method for Radiomic Data](https://mlsanity.com/paper/2302.09807/) (2023)

## Abstract (arXiv)

Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time consuming and subjective, this task is at the same time very challenging to solve for automatic segmentation methods. In this paper we present our most recent effort on developing a robust segmentation algorithm in the form of a convolutional neural network. Our network architecture was inspired by the popular U-Net and has been carefully modified to maximize brain tumor segmentation performance. We use a dice loss function to cope with class imbalances and use extensive data augmentation to successfully prevent overfitting. Our method beats the current state of the art on BraTS 2015, is one of the leading methods on the BraTS 2017 validation set (dice scores of 0.896, 0.797 and 0.732 for whole tumor, tumor core and enhancing tumor, respectively) and achieves very good Dice scores on the test set (0.858 for whole, 0.775 for core and 0.647 for enhancing tumor). We furthermore take part in the survival prediction subchallenge by training an ensemble of a random forest regressor and multilayer perceptrons on shape features describing the tumor subregions. Our approach achieves 52.6% accuracy, a Spearman correlation coefficient of 0.496 and a mean square error of 209607 on the test set.