Training a segmentation model on 3D medical data has a reputation for being painful. Between DICOM formatting, GPU configuration, and framework-specific boilerplate, most teams spend weeks on setup before a single training run starts. This tutorial shows the full process on Datature Nexus in under six minutes, no command line required.
What This Tutorial Covers
- Creating a medical 3D project in Datature Nexus
- Selecting and loading annotated volumetric data (CT or MRI)
- Configuring the training pipeline and choosing a model architecture
- Launching the training run and monitoring progress
- Reviewing model predictions on 3D volumes
Why 3D Segmentation Is Hard (and How Nexus Simplifies It)
3D medical segmentation models like Swin UNETR and nnU-Net require specific data preprocessing (resampling, normalization, patch extraction), GPU memory management for volumetric inputs, and careful augmentation strategies. Nexus handles all of this behind the scenes. You pick the data, pick the model, and start training. The platform manages data loading, augmentation, and distributed training automatically.
Use Cases
Organ segmentation for surgical planning. Tumor detection and volumetric measurement. Brain structure analysis for neurological research. Cardiac imaging for chamber segmentation. Any workflow where labeled DICOM or NIfTI data is the starting point and pixel-level 3D predictions are the goal.
