Shallow SegNet with bilinear interpolation and weighted cross-entropy loss for Semantic segmentation of brain tissue
Pranav Goyal · 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · 2022
Semantic segmentation using deep learning techniques is now state-of-the-art in medical image segmentation, especially for brain Magnetic Resonant images (MRI). SegNet, a fully convolutional neural network architecture, is widely used for image segmentation. However, it is less frequently used than the other popular competing approach of U-Net, for brain MRI segmentation. A few researchers have proposed a fusion of SegNet and U-Net architectures to combine their desirable properties for performance gains. In this paper, a different direction of research is undertaken. A simpler yet more accurate shallow SegNet architecture is proposed to yield promising segmentation performance on brain MRI. The proposed architecture uses a bilinear interpolation upsampling mechanism instead of max unpooling. Further, a modified cross-entropy loss is employed that is weighted differently for different classes. The class imbalance problem is effectively overcome using this weighted cross-entropy loss. Performance comparison of the proposed architecture with existing works indicates that the average dice factor is enhanced to 0.83 with an improvement of 0.11 over the baseline SegNet. It is demonstrated that the proposed shallow SegNet is a simpler yet more accurate model compared to both the existing SegNet and U-Net and could serve as a baseline for fine-grained image segmentation tasks.