Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

Brain tumors are one of the leading causes of death in adults. They come in various shapes and sizes from one patient to another. Sometimes they infiltrate surrounding normal tissues, making it challenging to delineate tumor boundaries. Despite extensive research, the prognosis is still low. Accurate and timely brain tumor segmentation is critical for treatment planning and disease progression monitoring. Automatic segmentation of brain tumors using deep learning methods has been shown to produce high-quality and reproducible segmentation results. Specifically, the encoder-decoder networks, like the U-Nets, have dominated the previous BraTS Challenges because of their superior performance. Due to the importance of high-quality segmentation, most state-of-the-art models focus more on pushing the boundaries of the current methods at the expense of computational complexity. The computational budget for practical applications is minimal, requiring technological solutions that balance accuracy and available computational resources. In this study, we extended the basic U-Net model in the nnU-Net by replacing the basic 3D convolution blocks with bottleneck units utilizing depthwise-separable convolutions. Furthermore, we introduced the shuffle attention mechanism in the skip connections to compensate for the slight loss in segmentation accuracy due to a reduction in number of parameters. Extensive experimental results of the BraTS 2020 dataset reviewed that the proposed modifications achieved competitive performance at a lower computational cost.

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.

Underwater acoustic sensor networks can enable many underwater applications such as oceanic exploration, undersea supervisory control and inspection and video streaming. Today, there is an ever growing interest in real-time video streaming applications. These applications require huge bandwidth and delay guarantees which are challenges in underwater sensor networks due to unique characteristics of underwater channel such as huge propagation delay, limited acoustic channel and high error probability. Multipath routing was proved to provide bandwidth aggregation and faster packet delivery in terrestrial networks. The objective of this work is to develop an interference aware multipath routing solution for bandwidth-limited high-delay underwater acoustic channel.