Brain Tumor Segmentation Using Partial Depthwise Separable Convolutions

Gliomas are the most common and aggressive form of all brain tumors, with medial survival rates of less than two years for the highest grade. While accurate and reproducible segmentation of brain tumors is paramount for an effective treatment plan and diagnosis, automatic brain tumor segmentation is challenging because the lesion can appear anywhere in the brain with varying shapes and sizes from one patient to another. Moreover, segmentation is only done by analyzing pixel intensity values of surrounding tissues, and the diffusing nature of aggressive brain tumors makes it even more challenging to delineate tumor boundaries. Nevertheless, deep learning methods have superior performance in automatic brain tumor segmentation. However, their boost in performance comes at the cost of high computational complexity. This paper proposes efficient network architecture for 3D brain tumor segmentation, partially utilizing depthwise separable convolutions to reduce computational costs. The experimental results on the BraTS 2020 dataset show that our methods could achieve comparable results with the state-of-the-art methods with minimum computational complexity. Furthermore, we provide a critical analysis of the current efficient model designs. The code for this project is available at https://github.com/tmagadza/partialDepthwiseNet.

Automated summary

This study proposes an efficient network architecture for brain tumor segmentation, which partially utilizes depthwise separable convolutions to reduce computational costs. The experimental results show comparable performance with the state-of-the-art methods while minimizing computational complexity. A critical analysis of current efficient model designs is also provided.