KI-Morph

Large-scale Image Analysis and AI

Aims & Objectives

KI-Morph seeks to develop cutting-edge algorithms and infrastructure to streamline large-scale image analysis, focusing on the efficient processing of 3D tomographic images. By automating and accelerating these processes, we aim to empower researchers with tools that reduce manual effort and improve the accuracy of scientific insights.

Background

X-ray tomography has revolutionized life sciences by enabling researchers to generate detailed 3D images at various scales, from individual cells to entire organisms. These images provide critical insights into the structure and function of biological specimens, aiding in discoveries across fields like biology, medicine, and environmental science.

However, while image acquisition using modern tomographs is fast and mostly automated, the subsequent image processing and analysis remain labor-intensive and time-consuming. Even experienced researchers often spend days or weeks manually analyzing the data, making this step a significant bottleneck in the research process. This is where KI-Morph aims to make a transformative impact by streamlining these processes.

Research Topics

Our research centers on four key areas that are integral to advancing large-scale image analysis in life sciences:

Infrastructure Development:
We are building a scalable software and hardware framework designed to handle the vast amounts of data produced by 3D imaging technologies. This infrastructure is essential to support high-performance computing and ensure the efficient processing of complex datasets.
AI Algorithms for Image Segmentation and Analysis:
We are creating sophisticated AI-driven algorithms that can automate the segmentation and interpretation of 3D image volumes. By leveraging artificial intelligence, we aim to reduce manual effort, enhance accuracy, and accelerate data analysis.
Pipeline Evaluation:
Our processing pipeline is tested rigorously using datasets from a broad range of life sciences disciplines. This ensures that our methods are applicable across different scientific domains, improving the robustness and versatility of the system.
Science Communication and Visualization:
We place a strong emphasis on making our findings accessible. Through interactive visualizations, we aim to bridge the gap between complex data and clear, communicable insights, fostering collaboration and understanding in the broader scientific community.

Funding

We gratefully acknowledge the support provided by the Federal Ministry of Education and Research (BMBF). This funding plays a crucial role in advancing our research and development efforts, allowing us to push the boundaries of image analysis technologies in the life sciences.