Software

We share and open-source published software developed in our group:

Schirmacher D, Armagan Ü, Zhang Y, Kull T, Auler M and Schroeder T (2024)
aiSEGcell: user-friendly deep learning-based segmentation of nuclei in transmitted light images
PLOS Computational Biology, 20(8):e1012361

aiSEGcell

Wehling A, Loeffler D, Zhang Y, Kull T, Donato C, Szczerba B, Ortega GC, Lee M, Moor A, Göttgens B, Aceto N. and Schroeder T (2022)
Combined single-cell tracking and omics improves blood stem cell fate regulator identification.
Blood. 10.1182/blood.2022016880

trackSeq

alerT

Kunz L and Schroeder T (2019)
A 3D tissue-wide digital imaging pipeline for quantitation of secreted molecules shows absence of CXCL12 gradients in bone marrow
Cell Stem Cell, 25(6):846-854

DDD

Coutu DL, Kokkaliaris KD, Kunz L and Schroeder T (2018)
Multicolor quantitative confocal imaging cytometry
Nature Methods, 15: 39-46

XiT

Hilsenbeck O, Schwarzfischer M, Loeffler D, Dimopoulos S, Hastreiter S, Marr C, Theis FJ and Schroeder T (2017)
fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy
Bioinformatics, 33: 2020-2028

fastER

Hilsenbeck O*, Schwarzfischer M*, Skylaki S*, Schauberger B, Hoppe PS, Loeffler D, Kokkaliaris KD, Hastreiter S, Skylaki E, Filipczyk A, Strasser M, Buggenthin F, Feigelman JS, Krumsiek J, van den Berg AJJ, Endele M, Etzrodt M, Marr C, Theis FJ* and Schroeder T* (2016)
Software tools for single-cell tracking and quantification of cellular and molecular properties
Nature Biotechnology, 34: 703-706

tTt and qTfy