NiftyNet

Download BRATS weights from Model Zoo

Format MRI sequences to NIfTI

Configure .ini file for segmentation

Run net_segment inference

Visualize results in ITK-SNAP

Load the GAN-based synthesis application

Input T1-weighted MRI volumes

Train the generator using paired MRI-CT data

Generate synthetic CT volumes

Verify Hounsfield Unit accuracy

Initialize a 3D HighRes3DNet architecture

Set up window-based sampling for large CT volumes

Apply spatial data augmentation (rotation/scaling)

Train until Dice score plateaus

Export segmentations as DICOM-RT

Convert ultrasound video to image sequences

Use the regression application for feature tracking

Train on annotated landmark data

Deploy model for real-time frame processing

Integrate with surgical navigation software

Select the super-resolution network template

Input pairs of low-res and high-res scans

Configure the loss function for perceptual similarity

Run the training pipeline

Evaluate signal-to-noise ratio improvements

Tile large WSIs into 2D patches

Use the NiftyNet classification/detection module

Apply color normalization augmentation

Run inference across all tiles

Reconstruct the probability map

Define landmark coordinates in CSV format

Use the regression application wrapper

Configure a heatmap-based regression loss

Train the CNN

Extract coordinates from peak probability maps