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

• URL: http://arxiv.org/abs/2010.13967v2
• Date: Mon, 23 Nov 2020 00:56:25 GMT
• Title: Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction
• Authors: Carlo Russo, Sidong Liu, Antonio Di Ieva
• Abstract summary: We propose a novel method aimed to feed Deep Convolutional Neural Networks (DCNN) with spherical space transformed input data. In this work, the spherical coordinates transformation has been applied as a preprocessing method. The LesionEncoder framework has been applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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