A deep learning framework for 3D brain tumor segmentation and survival prediction

Accurate and efficient segmentation of brain tumors is crucial for early diagnosis, personalized treatment planning, and improved survival rates. Brain tumors exhibit complex spatial and morphological variations, making automated segmentation a challenging task. This study introduces a dynamic segmentation network (DSNet), a novel 3D brain tumor segmentation framework that integrates adversarial learning, dynamic convolutional neural network (DCNN), and attention mechanisms to enhance precision and robustness. DSNet processes 3D magnetic resonance imaging (MRI) volumes, including T1-weighted (T1), T1-weighted with contrast enhancement (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) modalities, capturing rich spatial and contextual features. Leveraging adversarial training, DSNet refines boundary definitions, while dynamic filters adapt to tumor-specific heterogeneities, ensuring accurate segmentation across diverse cases. The attention mechanism further emphasizes tumor-relevant regions, enhancing feature extraction and boundary delineation. The model was trained and validated on the BraTS 2020 dataset, achieving dice similarity coefficients of 0.959, 0.975, and 0.947 for whole tumors (WT), tumor cores (TC), and enhancing tumor (ET) regions, respectively. Its generalizability was further confirmed through evaluations on the BraTS 2019 and BraTS 2018 datasets. Additionally, volumetric features derived from segmented images were used to predict patients’ overall survival rates via a Random Forest (RF) classifier. To enhance accessibility, we integrated the segmentation and prediction processes into a user-friendly web application. DSNet outperforms state-of-the-art methods, providing a robust and accurate solution for 3D brain tumor segmentation with strong clinical potential.