Accurate Automatic Glioma Segmentation in Brain MRI images Based on CapsNet

Glioma is a highly invasive type of brain tumor that appears in different parts of brain with various sizes, shapes, and blurred borders. Therefore, it is a challenging task to identify the exact boundaries of the tumor in an MR image. In recent years, deep learning based CNNs methods have gained popularity in the field of image processing and have been utilized for accurate image segmentation in medical applications. However, the inherent limitations of CNNs warrants the need for tens of thousands of images in the training phase, while the collection and annotation of such large number of images poses a great challenge. Here, for the first time, we have optimized a network based on the capsule neural network called SegCaps, to achieve accurate glioma segmentation in MR images. We have compared our results with a similar experiment conducted using commonly utilized U-Net. Both experiments are performed on the BraTS2020 challenging dataset. For U-Net, network training is performed on the entire dataset, while a subset containing only 20% of the whole dataset is used for the SegCaps. To evaluate the results of our proposed method, Dice Similarity Coefficient (DSC) is used. SegCaps and U-Net reached DSC of 87.96% and 85.56% on glioma tumor core segmentation, respectively. The SegCaps uses convolutional layers as the basic components and has the intrinsic capability to generalize novel viewpoints. The network learns the spatial relationship between features using dynamic routing of capsules. These capabilities of the capsule neural network have led to the 3% improvement of results in glioma segmentation with fewer data while it contains 95.4% less parameters than U-Net.