Abstract
Glioma imaging analysis is a challenging task. In this paper, we used the encoder-decoder structure to complete the task of glioma segmentation. The most important characteristic of the presented segmentation structure is that it can extract more abundant features, and at the same time, it greatly reduces the amount of network parameters and the consumption of computing resources. Different textures, first order statistics and shape-based features were extracted from the BraTS 2020 dataset. Then, we use cox survival analysis to perform feature selection on the extracted features. Finally, we use randomforest regression model to predict the survival time of the patients. The result of survival prediction with five-fold cross-validation on the training dataset is better than the baseline system.
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This work was supported by the Clinical Research Center of Shandong University. (No. 2020SDUCRCB002)
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Pang, E., Shi, W., Li, X., Wu, Q. (2021). Glioma Segmentation Using Encoder-Decoder Network and Survival Prediction Based on Cox Analysis. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_29
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