RESUMO
Precise delineation of brain glioblastoma or tumor through segmentation is pivotal in the diagnosis, formulating treatment strategies, and evaluating therapeutic progress in patients. Precisely identifying brain glioblastoma within multimodal MRI scans poses a significant challenge in the field of medical image analysis as different intensity profiles are observed across the sub-regions, reflecting diverse tumor biological properties. For segmenting glioblastoma areas, convolutional neural networks have displayed astounding performance in recent years. This paper introduces an innovative methodology for brain glioblastoma segmentation by combining the Dense-Attention 3D U-Net network with a fusion strategy and the focal tversky loss function. By fusing information from multiple resolution segmentation maps, our model enhances its ability to discern intricate tumor boundaries. Incorporating the focal tversky loss function, we effectively emphasize critical regions and mitigate class imbalance. Recursive Convolution Block 2 is proposed after fusion to ensure efficient utilization of all accessible features while maintaining rapid convergence. The network's effectiveness is assessed using diverse datasets BraTS 2020 and BraTS 2021. Results show comparable dice similarity coefficient compared to other methods with increased efficiency and segmentation performance. Additionally, the architecture achieved an average dice similarity coefficient of 82.4% and an average hausdorff distance (HD95) of 10.426, which demonstrated consistent performance improvement compared to baseline models like U-Net, Attention U-Net, V-Net and Res U-Net and indicating the effectiveness of proposed architecture.
