论文标题
在深卷积神经网络中预处理的球形坐标转换对脑肿瘤分割和生存预测的影响
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction
论文作者
论文摘要
预处理和数据增强在深度卷积神经网络（DCNN）中起重要作用。因此，几种方法旨在标准化和增强数据集，我们在这里提出了一种新型方法，旨在用球形空间转换输入数据，与标准的笛卡尔空间图像和体积相比，可以更好地促进特征学习。在这项工作中，球形坐标转化已被用作一种预处理方法，该方法与正常的MRI体积结合使用，提高了脑肿瘤分割的准确性和患者总体存活率（OS）对脑肿瘤细分（BRATS）挑战2020 Dataset的总体生存（OS）预测。然后将病变框架框架应用于自动从DCNN模型中提取功能，在验证数据集上实现了0.586 OS预测的精度，根据Brats 2020排行榜，这是最佳结果之一。
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.
