Interactive and computational analysis of large multiscale imaging data

Principal investigators: Xiaoyi Jiang, Lars Linsen
Project number: CRC 1450 Z01
Project term: 01/2021–12/2024

Graphical abstract
© CRC inSight

The multiscale imaging strategy central to this initiative imposes novel data analysis challenges. The high complexity of the acquired data results from their nature of being volumetric, time-varying, large, multiscale, and forming cohorts (1). Meeting these challenges requires basic research in the fields of image analysis, machine learning, and visualization (2, 3). Machine learning will be used to uncover inherent relationships between patterns at multiple scales. An interactive visual approach supports the user-centric analysis of detected features.

The deliverable of this project will be generally applicable, effective, and efficient methods supporting the overall goal of multiscale data analysis (4).

Blood vessels in the kidney tissue of a mouse. This abstract visualisation is based on images of a tissue sample taken using lightsheet fluorescence microscopy and was generated using 3D image processing techniques. These techniques were developed as part of our project to handle large high-resolution datasets in a way that enabels a simultaneously coarse and fine-scale analysis of vessel structures and quantitative comparison between multiple datasets. Here, the vessel tree volume was segmented before extracting the topology and specific vessel properties. The light blue spheres mark the ends and branch points of the blood vessels. A colour scale visualizes the different diameters of the vessels.
© Dominik Drees/Xiaoyi Jiang's research group, microscopy data by Nils Kirschnick/Friedemann Kiefer's research group


Principal investigators

Project members


The names of the principal investigators in our network have been bolded. Publications released prior to 2021, when funding for our network commenced, represent previous project-related work.


Nienkotter A, Jiang X. Kernel-Based Generalized Median Computation for Consensus Learning. IEEE Trans Pattern Anal Mach Intell 2022;PpAbstract
Schwarz C, Buchholz R, Jawad M, Hoesker V, Terwesten-Sole C, Karst U, Linsen L, Vogl T, Hoerr V, Wildgruber M, Faber C. Fingerprints of Element Concentrations in Infective Endocarditis Obtained by Mass Spectrometric Imaging and t-Distributed Stochastic Neighbor Embedding. ACS Infect Dis 2022Abstract


Bian A, Jiang X, Berh D, Risse B. Resolving Colliding Larvae by Fitting ASM to Random Walker-Based Pre-Segmentations. IEEE/ACM Trans Comput Biol Bioinform 2021;18: 1184-1194. Abstract
Drees D, Scherzinger A, Hagerling R, Kiefer F, Jiang X. Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. BMC Bioinformatics 2021;22: 346. Abstract
Kirschnick N, Drees D, Redder E, Erapaneedi R, Pereira da Graca A, Schafers M, Jiang X, Kiefer F. Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures. iScience 2021;24: 102650. Abstract


Jawad M, Molchanov V, Linsen L. Coordinated Image- and Feature-space Visualization for Interactive Magnetic Resonance Spectroscopy Imaging Data Analysis. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer 2019;SciTePress: 118-128. Abstract


Klemm S, Jiang X, Risse B. Deep distance transform to segment visually indistinguishable merged objects. In: Proc. of 40th German Conference on Pattern Recognition (GCPR), Stuttgart 2018: 422-433.
Matute J, Telea AC, Linsen L. Skeleton-Based Scagnostics. IEEE Trans Vis Comput Graph 2018;24: 542-552. Abstract
Scherzinger A, Hugenroth P, Rüder M, Bogdan S, Jiang X. Multi-class Cell Segmentation Using CNNs with F1-measure Loss Function. In: Proc. of 40th German Conference on Pattern Recognition (GCPR) 2018;Springer, Cham: 434-446. Abstract
Sheharyar A, Ruh A, Aristova M, Scott M, Jarvis K, Elbaz M, Dolan R, Schnell S, Lin K, Carr J, Markl M, Bouhali O, Linsen L. Visual analysis of regional anomalies in myocardial motion. In: Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association 2018: 135-144. Abstract


Hagerling R, Drees D, Scherzinger A, Dierkes C, Martin-Almedina S, Butz S, Gordon K, Schafers M, Hinrichs K, Ostergaard P, Vestweber D, Goerge T, Mansour S, Jiang X, Mortimer PS, Kiefer F. VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations. JCI Insight 2017;2: e93424. Abstract
Ristovski G, Matute J, Wehrum T, Harloff A, Hahn HK, Linsen L. Uncertainty visualization for interactive assessment of stenotic regions in vascular structures. Computers & Graphics 2017;69: 116-130. Abstract


Fofonov A, Molchanov V, Linsen L. Visual Analysis of Multi-Run Spatio-Temporal Simulations Using Isocontour Similarity for Projected Views. IEEE Trans Vis Comput Graph 2016;22: 2037-2050. Abstract


Tenbrinck D, Jiang X. Image segmentation with arbitrary noise models by solving minimal surface problems. Pattern Recognition 2015;48: 3293-3309. Abstract