2022

Laura Miesen, Péter Bándi, Brigith Willemsen, Fieke Mooren, Thiago Strieder, Eva Boldrini, Vedran Drenic, Jennifer Eymael, Roy Wetzels, Johannes Lotz, Nick Weiss, Eric Steenbergen, Toin H. Kuppevelt, Merijn Erp, Jeroen Laak, Nicole Endlich, Marcus J. Moeller, Jack F. M. Wetzels, Jitske Jansen, and Bart Smeets,
Parietal epithelial cells maintain the epithelial cell continuum forming Bowman's space in focal segmental glomerulosclerosis, Disease Models & Mechanisms , vol. 15, no. 3, 2022. The Company of Biologists.
DOI:10.1242/dmm.046342
File: 10.1242%2Fdmm.046342
Bibtex: BibTeX
@article{Miesen_2022, 
doi={10.1242/dmm.046342}, 
url={https://doi.org/10.1242%2Fdmm.046342}, 
year={2022}, 
month={mar}, 
publisher={The Company of Biologists}, 
volume={15}, 
number={3}, 
author={Miesen, Laura and Bándi, Péter and Willemsen, Brigith and Mooren, Fieke and Strieder, Thiago and Boldrini, Eva  and Drenic, Vedran and Eymael, Jennifer and Wetzels, Roy and Lotz, Johannes and Weiss, Nick and  Steenbergen, Eric and van Kuppevelt, Toin H. and van Erp, Merijn and van der Laak, Jeroen and Endlich, Nicole and Moeller, Marcus J. and Wetzels, Jack F. M. and Jansen, Jitske and Smeets, Bart}, 
title={Parietal epithelial cells maintain the epithelial cell continuum forming Bowman's space in focal segmental glomerulosclerosis}, 
journal={Disease Models & Mechanisms}
}

2021

André Homeyer, Johannes Lotz, Lars Ole Schwen, Nick Weiss, Daniel Romberg, Henning Höfener, Norman Zerbe, and Peter Hufnagl,
Artificial intelligence in pathology: From prototype to product, Journal of Pathology Informatics , vol. 12, no. 1, pp. 13, 2021. Elsevier BV.
DOI:10.4103/jpi.jpi_84_20
File: 10.4103%2Fjpi.jpi_84_20
Bibtex: BibTeX
@article{Homeyer2021, 
doi={10.4103/jpi.jpi_84_20}, 
url={https://doi.org/10.4103%2Fjpi.jpi_84_20}, 
year={2021}, 
publisher={Elsevier {BV}}, 
volume={12}, 
number={1}, 
pages={13}, 
author={Homeyer, André and Lotz, Johannes and Schwen, Lars Ole and Weiss, Nick and Romberg, Daniel and Höfener, Henning and Zerbe, Norman and Hufnagl, Peter}, 
title={Artificial intelligence in pathology: From prototype to product}, 
journal={Journal of Pathology Informatics}
}

2020

Jiří Borovec, Jan Kybic, Ignacio Arganda-Carreras, Dmitry V. Sorokin, Gloria Bueno, Alexander V. Khvostikov, Spyridon Bakas, Eric I-Chao Chang, Stefan Heldmann, Kimmo Kartasalo, Leena Latonen, Johannes Lotz, Michelle Noga, Sarthak Pati, Kumaradevan Punithakumar, Pekka Ruusuvuori, Andrzej Skalski, Nazanin Tahmasebi, Masi Valkonen, Ludovic Venet, Yizhe Wang, Nick Weiss, Marek Wodzinski, Yu Xiang, Yan Xu, Yan Yan, Paul Yushkevic, Shengyu Zhao, and Arrate Muñoz-Barrutia,
ANHIR: Automatic Non-rigid Histological Image Registration Challenge, IEEE Transactions on Medical Imaging , 2020. IEEE.
File: tmi.2020.2986331
Johannes Lotz,
Combined Local and Global Image Registration and its Application to Large-Scale Images in Digital Pathology, Institute of Mathematics and Image Computing, University of Lübeck, 2020.
Bibtex: BibTeX
@phdthesis{2020-PhD-Lotz,
  Author = {Johannes Lotz},
  Title = {Combined Local and Global Image Registration and its Application to Large-Scale Images in Digital Pathology},
  School = {Institute of Mathematics and Image Computing, University of Lübeck},
  Year = {2020},
}
C. Mercan, G.C.A.M. Mooij, D. Tellez, J. Lotz, N. Weiss, M. Gerven, and F. Ciompi,
Virtual Staining for Mitosis Detection in Breast Histopathology, IEEE, 2020.
File: isbi45749.2020.9098409
Bibtex: BibTeX
@inproceedings{Lotz2020ISBI,
  author={Mercan, C. and Mooij, G.C.A.M. and Tellez, D. and Lotz, J. and Weiss, N. and van Gerven, M. and Ciompi, F.},
  title={Virtual Staining for Mitosis Detection in Breast Histopathology},
  journal={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  publisher={IEEE}
  year={2020},
  url={https://doi.org/10.1109/isbi45749.2020.9098409},
}

2019

Wouter Bulten, Péter Bándi, Jeffrey Hoven, Rob Loo, Johannes Lotz, Nick Weiss, Jeroen Laak, Bram Ginneken, Christina Kaa, and Geert Litjens,
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard, Scientific Reports , vol. 9, 2019.
File: s41598-018-37257-4
Bibtex: BibTeX
@article{Lotz2019,
  author={Bulten, Wouter and Bándi, Péter and Hoven, Jeffrey and van de Loo, Rob and Lotz, Johannes and Weiss, Nick and van der Laak, Jeroen and van Ginneken, Bram and Hulsbergen-van de Kaa, Christina and Litjens, Geert },
  title={Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard},
  journal={Scientific Reports},
  year={2019},
  volume={9},
  article={864},
  url={https://doi.org/10.1038/s41598-018-37257-4},
}

2016

Judith M. Lotz, Franziska Hoffmann, Johannes Lotz, Stefan Heldmann, Dennis Trede, Janina Oetjen, Michael Becker, G{ü}nther Ernst, Peter Maas, Theodore Alexandrov, Orlando Guntinas-Lichius, Herbert Thiele, and Ferdinand Eggeling,
{Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition}, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics , 2016. Elsevier B.V..
DOI:10.1016/j.bbapap.2016.08.018
Bibtex: BibTeX
@article{Lotz2016Integration,
abstract = {In the last years, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) became an imaging technique which has the potential to characterize complex tumor tissue. The combination with other modalities and with standard histology techniques was achieved by the use of image registration methods and enhances analysis possibilities. We analyzed an oral squamous cell carcinoma with up to 162 consecutive sections with MALDI MSI, hematoxylin and eosin (H{\&}E) staining and immunohistochemistry (IHC) against CD31. Spatial segmentation maps of the MALDI MSI data were generated by similarity-based clustering of spectra. Next, the maps were overlaid with the H{\&}E microscopy images and the results were interpreted by an experienced pathologist. Image registration was used to fuse both modalities and to build a three-dimensional (3D) model. To visualize structures below resolution of MALDI MSI, IHC was carried out for CD31 and results were embedded additionally. The integration of 3D MALDI MSI data with H{\&}E and IHC images allows a correlation between histological and molecular information leading to a better understanding of the functional heterogeneity of tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.},
author = {Lotz, Judith M. and Hoffmann, Franziska and Lotz, Johannes and Heldmann, Stefan and Trede, Dennis and Oetjen, Janina and Becker, Michael and Ernst, G{\"{u}}nther and Maas, Peter and Alexandrov, Theodore and Guntinas-Lichius, Orlando and Thiele, Herbert and von Eggeling, Ferdinand},
doi = {10.1016/j.bbapap.2016.08.018},
issn = {15709639},
journal = {Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics},
keywords = {3D,Head and Neck Cancer,MALDI imaging,OSCC,image registration,immunohistochemistry,multimodality},
pmid = {27594533},
publisher = {Elsevier B.V.},
title = {{Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition}},
year = {2016}
}

2015

Nick Weiss, Johannes Lotz, and Jan Modersitzki,
{Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities}, in Bildverarbeitung für die Medizin 2015, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck , Handels, Heinz and Deserno, Thomas M. and Meinzer, Hans-Peter and Tolxdorff, Thomas, Eds. Berlin, Heidelberg: Springer Vieweg, 2015. pp. 245.
DOI:10.1007/978-3-662-46224-9
ISBN:9783662462232
Bibtex: BibTeX
@inproceedings{Weiss2015,
abstract = {Abstract. 3D reconstruction and digital double staining offer pathologists many new insights into tissue structure and metabolism. Key to these applications is the precise registration of histological slide images, that is challenging in several ways. One major challenge are differently stained slides, that highlight different parts of the tissue. In this paper we introduce a new registration method to face this multimodality. It abstracts the image information to cell nuclei densities. By minimizing the distance of these densities an affine transformation is determined that restores the lost spatial correspondences. The proposed density based registration is evaluated using consecutive histological slides. It is compared to a Mutual Information based registration and shown to be more accurate and robust.},
address = {Berlin, Heidelberg},
author = {Weiss, Nick and Lotz, Johannes and Modersitzki, Jan},
booktitle = {Bildverarbeitung f\"{u}r die Medizin 2015, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 15. bis 17. M\"{a}rz 2015 in L\"{u}beck},
doi = {10.1007/978-3-662-46224-9},
editor = {Handels, Heinz and Deserno, Thomas M. and Meinzer, Hans-Peter and Tolxdorff, Thomas},
file = {:home/jo/Seafile/misc/mendeley/Weiss, Lotz, Modersitzki - 2015 - Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities.pdf:pdf},
isbn = {9783662462232},
month = mar,
pages = {245},
publisher = {Springer Vieweg},
title = {{Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities}},
year = {2015}
}
J Lotz, J Olesch, B M{ü}ller, T Polzin, P Galuschka, J M Lotz, S Heldmann, H Laue, A Warth, B Lahrmann, N Grabe, O Sedlaczek, K Breuhahn, and J Modersitzki,
{Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images}, IEEE Transactions on Biomedical Engineering , vol. 63, no. 9, pp. 1812--1819, 2015.
DOI:10.1109/TBME.2015.2503122
File: TBME-00958-2015_preprint.pdf
Bibtex: BibTeX
@article{Lotz2015,
abstract = {Objective: Image Registration of whole slide histology images allows the fusion of fine-grained information - like different immunohistochemical stains - from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell-level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multi-stain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15 %, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multi-stain registration which allows to compare different anti-bodies at cell-level. available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multi-stain images.Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15 %, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multi-stain registration which allows to compare different anti-bodies at cell-level.},
author = {Lotz, J and Olesch, J and M{\"{u}}ller, B and Polzin, T and Galuschka, P and Lotz, J M and Heldmann, S and Laue, H and Warth, A and Lahrmann, B and Grabe, N and Sedlaczek, O and Breuhahn, K and Modersitzki, J},
doi = {10.1109/TBME.2015.2503122},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {computer-aided diagnosis,digital pathology,high resolution histological scans ,histopathology, image registration},
title = {{Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images}},
year = {2015},
volume = {63}, 
number = {9},
pages = {1812--1819}
}

2014

J Georgii, T Bartscherer, C Degel, H Fonfara, H Hewener, T Kipshagen, B Kocev, J Lotz, D Ojdanic, J Olesch, S Rothlübbers, D Speicher, S Tretbar, H Hahn, and M Günther,
Improving Breast Biopsies by Motion Tracking. Proc. of Image-Guided Interventions, in Proc. of Image-Guided Interventions (IGIC) , 2014. pp. 79-80.
Bibtex: BibTeX
@inproceedings{georgi2014IGIC,
author = {Georgii, J and Bartscherer, T and Degel, C and Fonfara, H and Hewener, H and Kipshagen, T and Kocev, B and Lotz, J and Ojdanic, D and Olesch, J and Rothlübbers, S and Speicher, D and Tretbar, S and Hahn, H and Günther, M},
title = { Improving Breast Biopsies by Motion Tracking. Proc. of Image-Guided Interventions},
booktitle = {Proc. of Image-Guided Interventions (IGIC)},
pages = {79-80},
year = {2014}
}
P Bronsert, K Enderle-Ammour, M Bader, S Timme, M Kuehs, A Csanadi, G Kayser, I Kohler, D Bausch, J Hoeppner, UT Hopt, T Keck, E Stickeler, B Passlick, O Schilling, CP Reiss, Y Vashist, T Brabletz, J Berger, J Lotz, J Olesch, M Werner, and UF Wellner,
Cancer cell invasion and EMT marker expression - a three-dimensional study of the human cancer-host interface -, The Journal of Pathology , 2014. John Wiley & Sons, Ltd.
File: path.4416
Bibtex: BibTeX
@article {PATH:PATH4416,
author = {Bronsert, P and Enderle-Ammour, K and Bader, M and Timme, S and Kuehs, M and Csanadi, A and Kayser, G and Kohler, I and Bausch, D and Hoeppner, J and Hopt, UT and Keck, T and Stickeler, E and Passlick, B and Schilling, O and Reiss, CP and Vashist, Y and Brabletz, T and Berger, J and Lotz, J and Olesch, J and Werner, M and Wellner, UF},
title = {Cancer cell invasion and EMT marker expression - a three-dimensional study of the human cancer-host interface -},
journal = {The Journal of Pathology},
publisher = {John Wiley & Sons, Ltd},
issn = {1096-9896},
url = {http://dx.doi.org/10.1002/path.4416},
pages = {},
keywords = {cancer cell invasion, epithelial-mesenchymal transition, tumor budding, human adenocarcinoma, three dimensional reconstruction},
year = {2014}
}
Johannes Lotz, Judith Berger, Herbert Thiele, Janine Olesch, Benedikt Müller, Kai Breuhahn, Arne Warth, Niels Grabe, Bernd Lahrmann, Oliver Sedlaczek, and Stefan Heldmann,
Elastic image registration on whole slide images for digital double staining and 3D reconstruction, in Poster Submission at the European Molecular Imaging Meeting - EMIM , 2014.
File: wwu
Bibtex: BibTeX
@inproceedings{lotz2014elastic,
  title={Elastic image registration on whole slide images for digital double staining and 3D reconstruction},
  author={Johannes Lotz and Judith Berger and Herbert Thiele and Janine Olesch and Benedikt Müller and Kai Breuhahn and Arne Warth  and Niels Grabe and Bernd Lahrmann and Oliver Sedlaczek and Stefan Heldmann
},
  booktitle={Poster Submission at the European Molecular Imaging Meeting - EMIM},
  year={2014},
  url = {http://s.fhg.de/wwu},
  organization={European Society for Molecular Imaging}
}
Johannes Lotz, Judith Berger, Benedikt M\"uller, Kai Breuhahn, Niels Grabe, Stefan Heldmann, Andr\'{e} Homeyer, Bernd Lahrmann, Hendrik Laue, Janine Olesch, Michael Schwier, Oliver Sedlaczek, and Arne Warth,
Zooming in: High Resolution 3D Reconstruction of Differently Stained Histological Whole Slide Images, in SPIE Medical Imaging , 2014. pp. 904104-1--7.
File: Lotz_Berger_-_Zooming_in__High_Resolution_3D_Reconstruction_of_Differently_Stained_Histological_Whole_Slide_Images_SPIE-submitted.pdf
Bibtex: BibTeX
@inproceedings{lotz2014zooming,
  title={Zooming in: High Resolution 3D Reconstruction of Differently Stained Histological Whole Slide Images},
  author={Johannes Lotz and Judith Berger and Benedikt M\"uller and Kai Breuhahn and Niels Grabe and Stefan Heldmann and Andr\'{e} Homeyer and Bernd Lahrmann and Hendrik Laue and Janine Olesch and Michael Schwier and Oliver Sedlaczek and Arne Warth
},
  booktitle={SPIE Medical Imaging},
  year={2014},
  organization={International Society for Optics and Photonics},
pages = {904104-1--7}
} 

2013

Johannes Lotz, Bernd Fischer, Janine Olesch, and Matthias Günther,
Real time motion analysis in 4D medical imaging using conditional density propagation, in SPIE Medical Imaging 2015: Image Processing , 2013. pp. 86690T-86690T-7.
DOI:10.1117/12.2006842
File: lotz2013real.pdf
Bibtex: BibTeX
@inproceedings{lotz2013real,
author = {Lotz, Johannes and Fischer, Bernd and Olesch, Janine and Günther, Matthias},
title = {Real time motion analysis in 4D medical imaging using conditional density propagation},
booktitle ={SPIE Medical Imaging 2015: Image Processing},
volume = {8669},
number = {},
pages = {86690T-86690T-7},
abstract = {Motion, like tumor movement due to respiration, constitutes a major problem in radiotherapy and/or diagnostics.
A common idea to compensate for the motion in 4D imaging, is to invoke a registration strategy, which aligns
the images over time. This approach is especially challenging if real time processing of the data and robustness
with respect to noise and acquisition errors is required.
To this end, we present a novel method which is based only on selected image features and uses a probabilistic
approach to compute the wanted transformations of the 3D images. Moreover, we restrict the search space to
rotation, translation and scaling.
In an initial phase, landmarks in the first image of the series have to be identified, which are in the course
of the scheme automatically transferred to the next image. To find the associated transformation parameters,
a probabilistic approach, based on factored sampling, is invoked. We start from a state set containing a fixed
number of different candidate parameters whose probabilities are approximated based on the image information
at the landmark positions. Subsequent time frames are analyzed by factored sampling from this state set and
by superimposing a stochastic diffusion term on the parameters.
The algorithm is successfully applied to clinical 4D CT data. Landmarks have been placed manually to mark
the tumor or a similar structure in the initial image whose position is then tracked over time. We achieve a
processing rate of up to 12 image volumes per second. The accuracy of the tracking after five time steps is
measured based on expert placed landmarks. We achieve a mean landmark error of less than 2 mm in each
dimension in a region with radius of 25 mm around the target structure.},
year = {2013},
doi = {10.1117/12.2006842},
URL = { http://dx.doi.org/10.1117/12.2006842},
eprint = {}
}
Judith Berger, Johannes Lotz, Janine Olesch, Mark Schenk Kai Breuhahn, Benedikt Müller, Arne Warth, Niels Grabe, Bernd Lahrmann, and Oliver Sedlaczek,
Virtual Double Staining Using Elastic Image Registration, 2013.
File: fraunhofer-mevis_pathologyvisions2013.pdf
Bibtex: BibTeX
@Misc{berger2013virtual,
  author = 	 {Judith Berger and Johannes Lotz and Janine Olesch and Mark Schenk
Kai Breuhahn and Benedikt Müller and Arne Warth and Niels Grabe and Bernd Lahrmann and
Oliver Sedlaczek},
  title = 	 {Virtual Double Staining Using Elastic Image Registration},
  note = {Poster Submission at Pathology Visions, San Antonio},
  year = 	 {2013},
  url = 	 {https://digitalpathologyassociation.org/poster-presentations}
}

 

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