AI Platform for Learning 3D Cellular Objects in High-Resolution Holo-Tomographic Volumes

A unique holo-tomographic microscope allows recording cellular 3D volumetric images and detecting unlabeled structures at an unprecedented subcellular resolution in both space and time ( Because of the lack of labels, powerful Artificial Intelligence (AI) is required to help experimenters to label 3D structures and pursue with quantitative analysis of their dataset. Thus, the AI-driven analysis of holo-tomographic cellular volumes will revolutionize the field of live cell imaging.



  • Develop an AI-framework with deep learning algorithms for learning 3D high-resolution cellular and subcellular details within holo-tomographic volumes
  • Benchmark both predictive and computational performance of developed algorithms
  • Based on “Best Software Practices” industrialize most promising AI-algorithm including graphical user interface (GUI) and System Tests

  • Familiarize with holo-tomographic microscopy – including practical experiments
  • Explore current Machine Learning (ML) and Deep Learning (DL) algorithms to learn subcellular 3D structures in holo-tomographic volumes
  • Setup AI-framework (workflow) with functionality:
    • Load and preprocess holo-tomographic data (provided by Nanolive)
    • Train-validate-test
    • Visualize and store
  • Implement Prototype including:
    • The currently existing ML-algorithms as starting point (base line)
    • The most promising ML – / DL-algorithm evaluated above
    • The possible extension of the initial ground truth data
    • Benchmark and iteratively improve implemented ML- / DL-algorithms
  • Industrialize most promising ML- / DL-algorithm using best Software Practices including:
    • Test Driven Development
    • Modularity
    • Extensibility: e.g. new features via on-line improving labelling quality
    • Ergonomic Graphical User Interfaces (GUI)
  • Document and present Master Thesis
    • Final document
    • One-Minute Video Summary
    • 30 minute presentation
Possible Technologies

  • Machine/Deep Learning and Software Engineering
  • Python, TensorFlow, mxNet
  • Optional: scikit-learn, ImageJ, MATLAB

  • Choi, W. et al. Tomographic phase microscopy. Nat. Methods 4, 717-719 (2007).
  • Cotte, Y. et al. Marker-free phase nanoscopy. Nat. Photonics 7, 113-117 (2013).
  • Chen, L,C. et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs IEEE Transactions on Pattern Analysis and Machine Intelligence

Kind of Work
40% Research, 40% Software Development, 20% Benchmarking

Machine Learning & Deep Learning Basics, CV, Python, Matlab

Time & Effort
Master’s Thesis, 1 Person


SCS - Studienarbeiten
SCS - Studienarbeiten