A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI

Publication date: Available online 12 June 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Tiejun Yang, Jikun Song, Lei LiAbstractThe segmentation of brain tumors in magnetic resonance imaging (MRI) images plays an important role in early diagnosis, treatment planning and outcome evaluation. However, due to gliomas’ significant diversity in structure, the segmentation accuracy is low. In this paper, an automatic segmentation method integrating the small kernels two-path convolutional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extraction ability of SK-TPCNN and the joint optimization capability of model are presented respectively. The SK-TPCNN structure combining the small convolutional kernels and large convolutional kernels can enhance the nonlinear mapping ability and avoid over-fitting, the multiformity of features is also increased. The learned features from SK-TPCNN are then applied to the RF classifier to implement the joint optimization. RF classifier effectively integrates redundancy features and classify each MRI image voxel into normal brain tissues and different parts of tumor. The proposed algorithm is validated and evaluated in the Brain Tumor Segmentation Challenge (Brats) 2015 challenge Training dataset and the better performance is achieved.

Source: Biocybernetics and Biomedical Engineering - June 12, 2019 Category: Biomedical Engineering Source Type: research