A Novel Unified 3D Nnu-Net Deep-Learning Model for Brain Tumor & Whole Brain Segmentation

Brain tumors are one of the most common neurological complications that affect millions each year. Current studies have focused only on either the segmentation of brain tumors or healthy anatomical regions. Unfortunately, there is a lack of research on whole brain segmentation of patients with existing tumors. The segmentation of these regions is cruical for understanding patient pathologies. Due to the distortions in the brain caused by tumors, whole-brain segmentation models trained on healthy brains will not perform accurately on diseased ones. To address this issue, this paper proposes a novel unified 3D nnU-Net model for the segmentation of both brain tumor and whole-brain structures for the first time. Training a model on datasets that include both healthy and cancerous brain regions could enhance the generalizability of machine learning (ML) models for performing both whole-brain segmentation and tumor segmentation tasks. Currently, there are no datasets that are publicly available for this purpose. This paper proposes to address this challenge by augmenting data from OASIS-1 and BraTS2023 to train the baseline model. The model successfully achieves a Dice Similarity Coefficient (DSC) of 0.814 for whole brain structures and 0.874 for tumor segmentation, demonstrating its effectiveness across diverse regions.