# Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

arXiv: https://arxiv.org/abs/1807.07716 · HTML: https://mlsanity.com/paper/1807.07716/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning](https://mlsanity.com/paper/2107.00719/) (2021)
- [CateNorm: Categorical Normalization for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.15858/) (2021)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation](https://mlsanity.com/paper/2108.06772/) (2021)
- [Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2109.12629/) (2021)
- [On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis](https://mlsanity.com/paper/2306.13276/) (2023)
- [Performance Analysis of UNet and Variants for Medical Image Segmentation](https://mlsanity.com/paper/2309.13013/) (2023)
- [RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs](https://mlsanity.com/paper/2103.08457/) (2021)

## Abstract (arXiv)

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.