Papers › Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction

Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction

16 Dec 2019arXiv:1912.07224archive 2025-07-28

Xiaoqing Guo, Chen Yang, Pak Lun Lam, Peter Y. M. Woo, Yixuan Yuan

Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas. However, due to the high degree variations of heterogeneous appearance and individual physical state, the segmentation of sub-regions and OS prediction are very challenging. To deal with these challenges, we utilize a 3D dilated multi-fiber network (DMFNet) with weighted dice loss for brain tumor segmentation, which incorporates prior volume statistic knowledge and obtains a balance between small and large objects in MRI scans. For OS prediction, we propose a DenseNet based 3D neural network with position encoding convolutional layer (PECL) to extract meaningful features from T1 contrast MRI, T2 MRI and previously segmented subregions. Both labeled data and unlabeled data are utilized to prevent over-fitting for semi-supervised learning. Those learned deep features along with handcrafted features (such as ages, volume of tumor) and position encoding segmentation features are fed to a Gradient Boosting Decision Tree (GBDT) to predict a specific OS day

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Guo-Xiaoqing/BraTS_OS officialmentioned in papermentioned on GitHub report

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Brain Tumor Segmentation Prognosis Segmentation Survival Prediction Tumor Segmentation

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1x1 Convolution Average Pooling Batch Normalization Concatenated Skip Connection Convolution Dense Block Dense Connections Dice Loss Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Softmax

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