Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
This work addresses clinical challenges in glioma diagnosis and prognosis, but it is incremental as it builds on existing deep learning methods with minor modifications.
The paper tackles brain tumor segmentation and overall survival prediction from MRI scans using a 3D dilated multi-fiber network with weighted dice loss for segmentation and a DenseNet-based network with position encoding for feature extraction, achieving improved performance in these tasks.
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