Brain Tumor Segmentation in MRI Images using Deformable and Dilated Convolutions

Segmentation of brain tumor images is an important issue in medical image processing and can help surgeons to accurately assess the tumor area. Since tumors vary in shape, size, and location from patient to patient, segmenting brain tumors is a challenge. In addition, small tumors are more difficult to segment than larger ones. In this paper, we present a method based on deep convolutional networks to improve the segmentation accuracy of brain tumors, especially small tumors in MRI images. In this method we have increased the accuracy of tumor segmentation by adding a module to UNet model. The proposed module uses deformable and dilated convolutions, which provide more spatial information to the network and thus increase the accuracy of tumor segmentation. The results show that our method is able to achieve a Dice of 0.8877 in the whole tumor section. For the core and enhancing tumor sections, we were able to achieve Dice values of 0.8683 and 0.8176, respectively.