Multimodal Brain Tumour Segmentation using Densely Connected 3D Convolutional Neural Network
Mina Ghaffari, Arcot Sowmya, Ruth Oliver, Len Hamey · 2019
Reliable brain tumour segmentation methods from brain scans are essential for accurate diagnosis and treatment planning. In this paper, we propose a semantic segmentation method based on convolutional neural networks for brain tumour segmentation using multimodal brain scans. The proposed model is a modified version of the well-known U-net architecture. It gains from DenseNet blocks between the encoder and decoder parts of the U-net to transfer more semantic information from the input to the output. In addition, to speed up the training process, we employed deep supervision by adding segmentation blocks at the end of the decoder layers and summing up their outputs to generate the final output of the network. We trained and evaluated our model using the BraTS 2018 dataset. Comparing the results from the proposed model and a generic U-net, our model achieved higher segmentation accuracy in terms of the Dice score.