Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Abstract of Brain Tumor Segmentation Using Convolutional Neural Networks
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.Magnetic Resonance Imaging is the preferred imaging modality for assessing brain tumors, segmentation is necessary for diagnosis and treatment planning. Hierarchical segmentation approaches firstly segment the whole tumor, followed by intra-tumor tissue identification.
However, results comparing it with single stages approaches are needed, as state of the art results are also achieved by all-at-once strategies.
Currently, fully convolutional networks approaches for segmentation are very efficient. In this paper, a hierarchical approach for brain tumor segmentation using a fully convolutional network is studied.
Results show benefits from segmenting the complete tumor first, over all tissues in one stage.Moreover, the tumor core also benefits from such approach.
Conclusions
In summary, we propose a novel CNN-based method for segmentation of brain tumors in MRI images. We start by a pre-processing stage consisting of bias field correction, intensity and patch normalization.
After that, during training, the number of training patches is artificially augmented by rotating the training patches, and using samples of HGG to augment the number of rare LGG classes.
The CNN is built over convolutional layers with small 3×3 kernels to allow deeper architectures.
