Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN

Segmentation of multimodal brain tissues from 3D medical images is of great significance for brain diagnosis. It is required to create an automated and accurate segmentation based on deep-learning network due to the manual segmentation is time-consuming. However, how to segment medical images accurately and how to build neural network effectively with very limited computing resource, is still challenging task. To address these problems, we propose a novel model based on 3D fully convolutional network. More specifically, we apply multi-pathway architecture to feature extraction so as to effectively extract features from multi-modal MRI images. Different receptive field of feature have been extracted by adopting 3D dilated convolution in each pathway. By evaluating one-pathway model and key components of our model with a set of effective training schemes, we analyzed how these alternative methods affect the performance of experiments. Our proposed model was evaluated in the Brain Tumor Segmentation 2019 dataset (BraTS 2019), making an effective segmentation for the complete, core and enhancing tumor regions in Dice Similarity Coefficient metric (0.89, 0.78, 0.76) for the dataset. Also, we made a practice on BraTS 2018 using the same method with the Dice Similarity Coefficient metric of 0.90, 0.79, and 0.77 for the complete, core and enhancing tumor regions. Our method is inherently general and is a powerful tool to studies of medical images of brain tumors. Our code is available at https://github.com/JalexDooo/ BrainstormTS.