Brain Tumor Segmentation and Survival Prediction
This work addresses medical image analysis for brain tumor patients, but it is incremental as it applies existing methods like fully convolutional networks and random forests to a known dataset with modest improvements.
The paper tackles brain tumor segmentation and survival prediction by using a fully convolutional neural network with a three-layer encoder-decoder architecture and dense connections to segment gliomas on the BraTS 2019 dataset, achieving Dice Similarity scores of 0.92, 0.90, and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively, and then applies radiomic features, segmentation results, and age with a random forest regressor to predict patient survival classes, reporting 55.4% classification accuracy for a specific subset.
The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at encoder part to propagate the information from coarse layer to deep layers. This architecture is used to train three tumor sub-components separately. Subcomponent training weights are initialized with whole tumor weights to get the localization of the tumor within the brain. At the end, three segmentation results were merged to get the entire tumor segmentation. Dice Similarity of training dataset with focal loss implementation for whole tumor, tumor core and enhancing tumor is 0.92, 0.90 and 0.79 respectively. Radiomic features along with segmentation results and age are used to predict the overall survival of patients using random forest regressor to classify survival of patients in long, medium and short survival classes. 55.4% of classification accuracy is reported for training dataset with the scans whose resection status is gross-total resection.