An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients based on Volumetric and Shape Features

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries


Springer International Publishing

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An automatic overall survival time prediction system for Glioma brain tumor patients is proposed and developed based on volumetric, location, and shape features. The proposed automatic prediction system consists of three stages: segmentation of brain tumor sub-regions; features extraction; and overall survival time predictions. A deep learning structure based on a modified 3 Dimension (3D) U-Net is proposed to develop an accurate segmentation model to identify and localize the three Glioma brain tumor sub-regions: gadolinium (GD)-enhancing tumor, peritumoral edema, and necrotic and non-enhancing tumor core (NCR/NET). The best performance of a segmentation model is achieved by the modified 3D U-Net based on an Accumulated Encoder (U-Net AE) with a Generalized Dice-Loss (GDL) function trained by the ADAM optimization algorithm. This model achieves Average Dice-Similarity (ADS) scores of 0.8898, 0.8819, and 0.8524 for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively, in the train dataset of the Multimodal Brain Tumor Segmentation challenge (BraTS) 2020. Various combinations of volumetric (based on brain functionality regions), shape, and location features are extracted to train an overall survival time classification model using a Neural Network (NN). The model classifies the data into three classes: short-survivors, mid-survivors, and long-survivors. An information fusion strategy based on features-level fusion and decision-level fusion is used to produce the best prediction model. The best performance is achieved by the ensemble model and shape features model with accuracies of (55.2%) on the BraTS 2020 validation dataset. The ensemble model achieves a competitive accuracy (55.1%) on the BraTS 2020 test dataset.


Artificial intelligence; Brain functionality regions; Deep learning; MRI images; Neural Network; U-Net


Health Information Technology | Medicine and Health Sciences



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