MP-TDN: Multi-path tumor delineation network for brain tumor segmentation using bidirectional approach

Glioblastoma is a high-grade brain tumor that causes a high risk of death. Early detection of such tumors helps to improve human life. MRI scans are one of the most popular diagnostic reports to identify such kind of complex diseases. Such advanced diagnosis reports help to identify tumor size, location, and aggressiveness. The proposed architecture uses a hybrid bidirectional approach to share the feature with prior and domain branches. Domain branch focuses on the volumetric context for enhancing the boundary of tumor. Prior branch works on 2D and 3D fusion to identify spatial information for highly affected cells. Identification of sharp boundaries of the tumor is challenging. Based on the intensity of all modalities, the Residual Feature Interaction Network (RFIN) focuses on the non-enhancing regions. On the other end, based on the spatial information Domain Knowledge Interaction Network (DKIN) component focuses on WT. RFIN and DKIN act in a bi-directional manner for better identification of the region. The proposed architecture gives remarkable results based on benchmark datasets BraTS2019 and BraTS2020. For evaluation of the proposed approach, the mean DSC is considered, and the results are 0.8184, 0.8735, and 0.8902 for ET, WT, and TC, respectively.