NiftyNet is an open-source deep-learning platform developed by the University College London (UCL) for medical image analysis. It is built on top of TensorFlow and provides a modular framework for designing, training, and deploying convolutional neural networks (CNNs) for biomedical imaging tasks such as segmentation, classification, and regression.

Features of NiftyNet:

• Specialized for medical image analysis (2D, 3D, and multi-modal data).
• Built on TensorFlow for deep learning model development.
• Includes pre-trained models for tasks like organ segmentation and disease detection.
• Supports data loading, preprocessing, augmentation, and evaluation out of the box.
• Extensible architecture for rapid research prototyping.

To install NiftyNet, run these commands: $ sudo su $ cd /opt $ source niftynet-env/bin/activate $ pip show niftynet

Disclaimer: NiftyNet is an open-source research platform developed by the UCL Centre for Medical Image Computing. It is not actively maintained and may require older TensorFlow versions (e.g., TensorFlow 1.15). The software is provided "as is," without any warranties or guarantees. Users must ensure compliance with licensing, data privacy, and medical data handling regulations before deployment.