NiftyNet: a deep-learning platform for medical imaging (1709.03485v2)

Abstract: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.

• The paper introduces NiftyNet, a modular open-source framework that standardizes and streamlines medical image analysis.
• It leverages TensorFlow to provide efficient data handling, network design, and specialized evaluation metrics for segmentation, regression, and image generation.
• The platform enhances reproducible research and innovation by reducing duplicative efforts and promoting scalable, consistent methodology in medical imaging.

NiftyNet: A Deep-Learning Platform for Medical Imaging

The paper introduces NiftyNet, an open-source deep-learning framework tailored for medical imaging applications. The platform responds to the growing use of deep learning in the domain and the unique demands that come with it. Built on the TensorFlow library, NiftyNet offers a modular pipeline and seeks to streamline medical image analysis while providing a common framework for research dissemination.

Key Contributions and Methodologies

NiftyNet is structured around three fundamental objectives: facilitating efficient research, reducing duplicative efforts, and providing a standardized platform for sharing deep-learning tools. It supports various applications such as segmentation, regression, and generative modeling, which collectively address the segmentation of medical images, prediction of imaging metrics, and the creation of synthesized images.

The platform's architecture encapsulates several core components:

• Data Handling: NiftyNet incorporates specialized components for data loading and augmentation, addressing the peculiarities of medical imaging data such as non-standard file formats and the need for sophisticated pre-processing techniques.
• Network Design: It encapsulates pre-existing network architectures commonly used in medical imaging, enabling both ease-of-use for novices and flexibility for experts. By taking advantage of TensorFlow’s computational efficiencies, it optimizes both memory usage and processing speed for high-dimensional medical images.
• Evaluation Metrics: The platform provides built-in metrics tailored to the medical field, including various error measures and descriptive analyses relevant to medical imaging integrity.

Illustrative Applications

Three primary applications demonstrate NiftyNet's capabilities:

1. Segmentation: Utilizing a Dense V-network, the platform effectively performs multi-organ segmentation on abdominal CT images, delivering compelling dice scores and distance metrics illustrative of its segmentation precision.
2. Image Regression: NiftyNet is applied to synthesize CT images from MRI data, achieving mean absolute errors on par with, or superior to, existing methods. Such applications allow for improved workflows where certain imaging modalities might be unavailable.
3. Image Generation: With GANs, NiftyNet generates realistic ultrasound images based on specified anatomical poses. This application illustrates the platform's capability in training simulations and educational tools in a clinical setting.

Implications and Future Directions

NiftyNet’s implications are manifold. Practically, it enhances the reproducibility and generalizability of deep-learning studies in medical imaging by offering a shared platform for development and deployment. Theoretically, its modular design encourages innovative research, reducing barriers to entry for new researchers and aligning efforts across the field towards common objectives.

Future developments will likely include the expansion of its model zoo, integration of more application types, and the refinement of experimental designs such as automatic data partitioning and hyper-parameter optimization. Such advancements will further enhance the utility and adaptability of NiftyNet in the rapidly evolving landscape of AI and medical imaging.

In summary, NiftyNet addresses critical needs in medical image analysis, offering a comprehensive, flexible platform that prioritizes usability, scalability, and innovation. Its development represents a significant step toward standardized and efficient methodologies in medical imaging, signifying a substantial contribution to the field.