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).
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.
2022
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
2021
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
2019
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
2018
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
2017
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
2016
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
2015
Tenbrinck D, Jiang X. Image segmentation with arbitrary noise models by solving minimal surface problems. Pattern Recognition 2015;48: 3293-3309. Abstract
