zea: A Toolbox for Cognitive Ultrasound Imaging
Tristan S.W. Stevens, Wessel L. van Nierop, Ben Luijten, Vincent van de Schaft, Ois\'in Nolan, Beatrice Federici, Louis D. van Harten, Simon W. Penninga, Noortje I.P. Schueler, Ruud J.G. van Sloun
TL;DR
Zea is a versatile Python toolbox for cognitive ultrasound imaging, enabling flexible data processing pipelines and deep learning integration across major frameworks.
Contribution
It introduces a modular, differentiable ultrasound imaging pipeline with pre-defined models and multi-backend support, simplifying research and development.
Findings
Supports custom ultrasound reconstruction pipelines
Integrates seamlessly with TensorFlow, PyTorch, and JAX
Provides comprehensive documentation for users
Abstract
We present zea (pronounced ze-yah), a Python package for cognitive ultrasound imaging that offers a flexible, modular, and differentiable pipeline for ultrasound data processing. Additionally, it includes a collection of pre-defined models for ultrasound image and signal processing. The toolbox is designed to be easy to use, with a high-level interface that enables users to define custom ultrasound reconstruction pipelines and integrate deep learning models seamlessly. Built on top of Keras 3, it supports all three major deep learning backends: TensorFlow, PyTorch, and JAX, making it straightforward to incorporate custom ultrasound processing pipelines into machine learning workflows. Documentation is available at https://zea.readthedocs.io/.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.