@article{schafer2024overcoming,
  title={Overcoming data scarcity in biomedical imaging with a foundational multi-task model},
  author={Schäfer, Raphael and Nicke, Till and Höfener, Henning and Lange, Annkristin and Merhof, Dorit and Feuerhake, Friedrich and Schulz, Volkmar and Lotz, Johannes and Kiessling, Fabian},
  journal={Nature Computational Science},
  volume={4},
  number={7},
  pages={495--509},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@article{WeitzEtAl2024,
	author = {Philippe Weitz and Masi Valkonen and Leslie Solorzano and Circe Carr and Kimmo Kartasalo and Constance Boissin and Sonja Koivukoski and Aino Kuusela and Dusan Rasic and Yanbo Feng and Sandra Sinius Pouplier and Abhinav Sharma and Kajsa Ledesma Eriksson and Stephanie Robertson and Christian Marzahl and Chandler D. Gatenbee and Alexander R.A. Anderson and Marek Wodzinski and Artur Jurgas and Niccolò Marini and Manfredo Atzori and Henning Müller and Daniel Budelmann and Nick Weiss and Stefan Heldmann and Johannes Lotz and Jelmer M. Wolterink and Bruno {De Santi} and Abhijeet Patil and Amit Sethi and Satoshi Kondo and Satoshi Kasai and Kousuke Hirasawa and Mahtab Farrokh and Neeraj Kumar and Russell Greiner and Leena Latonen and Anne-Vibeke Laenkholm and Johan Hartman and Pekka Ruusuvuori and Mattias Rantalainen},
	journal = {Medical Image Analysis},
	pages = {103257},
	title = {The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue},
	volume = {97},
	year = {2024}}

@misc{nicke2024tissueconceptssupervisedfoundation,
      title={Tissue Concepts: supervised foundation models in computational pathology}, 
      author={Till Nicke and Jan Raphael Schaefer and Henning Hoefener and Friedrich Feuerhake and Dorit Merhof and Fabian Kiessling and Johannes Lotz},
      year={2024},
      eprint={2409.03519},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2409.03519}, 
}

@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}
}

@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}
}

@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},
}

@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},
}

@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},
}

@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}
}

@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}
}

@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}
}

@article{laue2015software,
  title={Software support for combined staging of lung cancer in CT, functional MRI and pathology},
  author={Laue, Hendrik OA and Kohlmann, Peter and Lotz, Johannes and Sedlaczek, Oliver and Mueller, Benedikt and Breuhahn, Kai and Grabe, Niels and Warth, Arne and Hahn, Horst},
  year={2015}
}

@article{gunther2015towards,
  title={Towards MR-guided biopsy outside the MR-scanner},
  author={Günther, M and Bartscherer, T and Georgii, J and Hewener, H and Kipshagen, T and Ojdanic, D and Kocev, B and Lotz, J and Olesch, J and Rothlübbers, S and others},
  year={2015}
}

@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}
}

@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}
}

@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}
}

@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}
} 

@article{5c9d7318d40e4b20ad69ccaaba9f5e77,
title = "3D reconstruction of lung adenocarcinomas—one module for the development of mathematical multiscale models of lung cancer",
abstract = "Aims The BMBF-funded LungSysII consortium integrates information derived from molecular biology, cell biology, and histology using systems biology approaches to generate integrative multiscale-models of non-small cell lung cancer (NSCLC). In this context, we aim to define the three dimensional spatial relationship of the vascular system and the tumor mass in human pulmonary adenocarcinomas (ADC) as well as adjacent non-tumorous tissues based on histological data. We here report our most recent progress to generate a comprehensive 3-dimensional (3D) picture of ADC. Methods. Material was collected from freshly resected ADC patients and systematically cut into pieces of up to 1 cm in diameter. Samples were processed for optical projection tomography (OPT) scanning, utilizing tissue autofluorescence or specific epitope staining using directly labelled smooth muscle actin (SMA)-specific antibodies. In addition, alternate staining of serial sections derived from tumor samples were performed including H&E-, factor VIII (FVIII)-, and pan-cytokeratin (KL1) staining. Automated whole slide imaging was performed using the Hamamatsu NanoZoomer Digital Pathology system. The resulting 2D information was used to generate a 3D representation of the data by means of a non-linear elastic image registration. Results. Whole tissue OPT-scanning revealed the spatial distribution of bronchial and vasculature structures in the tumor and adjacent non-tumorous lung tissue. The image quality of the 3D vessel structure was improved solving a non-negatively constrained, L2-based reconstruction problem iteratively (MRNSD) on the raw-data produced by the OPT system. To reconstruct the serial sectioning data to a 3D volume, a special non-linear image registration algorithm was developed and applied. Specialization of the algorithm was needed due to cutting artefacts such as shape distortion and staining variation. The optimized non-linear algorithm was successfully applied on the H&E-, FVIII-, and KL1-staining. Conclusions. We here present approaches for 3D reconstruction of the vascular system and tumor mass in ADC as well as bordering healthy tissue. This quantitative information covers the range μm to cm and can be used for computational tissue modelling and for integration in mathematical multiscale models, which are currently under development.",
author = "B Mueller and J Olesch and J Lotz and S Barendt and O Sedlaczek and B Lahrmann and N Grabe and F Bestvater and U Kauczor and Schnabel, {P A} and H Hoffmann and B Fischer and P Schirmacher and A Warth and K Breuhahn",
year = "2013",
month = may,
day = "1",
doi = "10.1007/s00292-013-1765-2",
language = "English",
volume = "34 ",
pages = "6--163",
journal = "Der Pathologe",
issn = "1432-1963",
number = "Suppl 1",
}

@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 = {}
}

@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}
}