Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction

TL;DR

This paper introduces a spherical coordinates transformation as a novel preprocessing step for deep CNNs in brain tumor segmentation and survival prediction, demonstrating improved accuracy on the BraTS 2020 dataset.

Contribution

It proposes a spherical space transformation method for preprocessing MRI data, enhancing feature learning in CNNs for brain tumor analysis.

Findings

Improved segmentation accuracy using spherical transformation.

Achieved 0.586 accuracy in survival prediction, ranking high on BraTS 2020 leaderboard.

Spherical preprocessing outperforms standard Cartesian methods.

Abstract

Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN). Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overall survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020 dataset. The LesionEncoder framework has been then applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction on the validation data set, which is one of the best results according to BraTS 2020 leaderboard.