Yaru3DFPN: a lightweight modified 3D UNet with feature pyramid network and combine thresholding for brain tumor segmentation

Gliomas are the most common and aggressive form of all brain tumors, with a median survival rate of fewer than two years, especially for the highest-grade glioma patient. Accurate and reproducible brain tumor segmentation is essential for an effective treatment plan and diagnosis to reduce the risk of further spread. Automated brain tumor segmentation is challenging because it can appear in the brain with variations in shape, size, and position from one patient to another. Several deep learning architectures have been created to handle automatic segmentation with good performance results on 3D MRI images. However, these architectures are generally large and require high hardware specifications and a large amount of memory and storage. This paper proposes a lightweight modified 3D UNet architecture with an outstanding performance level called Yaru3DFPN. The architecture is built based on the UNet. The block used is ResNet and is modified to use pre-activation strategies and GroupNormalization for batch normalization. In the expanding section, features are arranged into pyramid features. The final output is thresholded using the combining thresholding method. This architecture is light and fast. This proposal was tested using BraTS datasets with the highest dice performance of 80.90%, 86.27%, and 92.02% for ET, TC, and WT areas, respectively. This result outperformed all other comparative architectures and promised to be developed for clinical application.