This project compares the performance of UNet, ResUNet, SegResNet, and UNETR architectures on the 2017 LiTS dataset for liver tumor segmentation. We evaluate segmentation accuracy using the DICE score to identify key factors for effective tumor segmentation.

3D multi-label brain tumor segmentation from multimodal MRI (FLAIR, T1w, T1gd, T2w) using Decathlon data. End-to-end workflow with 3D SegResNet, Dice loss, and Mean Dice evaluation for glioma sub-region segmentation (edema, core, necrotic/cystic, enhancing). Focused on complex, heterogeneous tumor localization in clinical neuroimaging.

🧠 3D Brain Tumor Segmentation with MONAI | BRATS 2020 Baseline UNet, UNet++, SegResNet 🚀

Brain tumor segmentation on BraTS-2023 dataset using nnUNet, SegResNet, and SwinUNETR. Models trained and evaluated on MRI scans to segment tumor sub-regions: WT, TC, ET, and RC, with fold-wise performance visualizations.