Improving Lung Imaging with AI

Developing and Testing Super-Resolution AI Techniques for Refined Depiction of the Bronchial Tree

Contact

Niklas Koser, University of Kiel Claus Glüer, University of Kiel

Description

The Intelligent Imaging Lab (i2Lab) of the Section Biomedical Imaging, Department of Radiology and Neuroradiology, UKSH, and CAU Kiel is developing innovative artificial intelligence methods for radiological imaging. Here we address super-resolution imaging. This AI approach may allow to improve the image quality (without increasing the radiation dose) or reduce the radiation dose (at the same image quality).

In the challenge, participating teams will use a pretrained Artificial Neural Network to segment the bronchial tree and try to program a super-resolution approach for improving image quality to improve the segmentation result.

Dataset

60 lung CT images and segmentation mask from public repositories will be provided by us (40 for training/validation and 20 for testing)

How you can contribute

Single competitors or teams with some experience in building ML models are invited to participate. Experience in PyTorch or TensorFlow would be beneficial.