Image of Constantin Pape

I am a Juniorprofessor at Georg-August University Göttingen and a member of the Institute of Computer Science and CIDAS. I lead the research group for Computational Cell Analytics since March 2022. My research is focused on deep learning methods for computer vision, in particular segmentation, applied primarily to microscopy images.

Previously, I have worked as a PostDoc at EMBL Heidelberg in the group of Anna Kreshuk and did my PhD at the University of Heidelberg under the supervision of Anna Kreshuk and Fred Hamprecht. During my PhD and PostDoc I have mainly worked on instance segmentation problems, with a focus on large volumetric electron microscopy data.

Constantin Pape

Institute of Computer Science
Georg-August University Göttingen

Jobs, thesis, student projects

Job alert: We are looking for a staff scientist to implement AI solutions for serological assays with applications in Covid-19 and beyond. This is a joint project with Vibor Laketa and Anna Kreshuk and the position will be located in Heidelberg. See the abstract below and check out for more details on the funding.

Abstract of the AIH project

Looking for student assistants for the course “Deep Learning for Computer Vision”: We are looking for student assistants for the “Deep Learning for Computer Vision” course at the beginning of the winter semester. The estimated amount of work for the semester is 72 hours (e.g. 6 months à 12 h/month or 4 months à 18 h/month) or, if you want to, two workloads and with this 144 hours. The work includes supervising the tutorials (incl. time for preparation), answering students’ questions in RocketChat and help with exam correction. The tutorials will take place in person, in the CIP pool of the IfI. We may also offer an online tutorial via BigBlueButton. Ideally, you have already attended the courses “Machine Learning” and “Deep Learning” (name of the course up until winter term 2021/22) / “Deep Learning for Computer Vision”, but this is not a mandatory requirement. You should have basic knowledge in the field of Machine Learning/Deep Learning and enjoy and be interested in the topic as well as in the supervision of students! If you are interested, just send us a short mail to We are looking forward to hearing from you!

I am currently at capacity with supervision of master or bachelor thesis, but if you're interested to work on a smaller project (that could be the prepartion for a thesis at a later point) feel free to reach out. Here are some examples for current projects of students in the group:

  1. Benchmarking segmentation methods for live cell microscopy
  2. Self-supervised learning for cell instance segmentation
  3. Benchmark cell tracking methods for live cell microscopy
  4. Embedding based cell tracking
  5. Segmentation of myelinated axons in EM
  6. Mitochondria segmentation in EM
  7. Self-supervised segmentation with Masked Auto Encoders


My main research interest is in high-quality segmentation methods for microscopy that require minimal supervision. The adoption of deep learning has improved the quality of image analysis for microscopy dramatically in the last decade. In the same period, the throughput, field of view and time resolution of microscopes has also increaded significantly, requiring the automation of key analysis steps, such as the identification of cells or other structures of interest through segmentation. While deep-learning based methods yield high enough quality to achieve this automation in many instances, they require large amounts of (manually) annotated training data . This limitation makes them impractical for many applications where no such training data is available and is too costly to create. Fortunately, the last years have seen a large reseach interest in weak supervision, domain adaptation and self-supervision, which promises to make training of high-quality models with significantly less annotated data possible. However, these methods are mostly developed for natural images and their adoption for microscopy has so far been limited. I aim to bridge this gap, building on initial work for domain adaptation, weak supervision and even reinforcement learning.

Image visualising 4 different image analysis resutlts.

My previous research has been focused on boundary based instance segmentation. I have developed graph-based methods, using graph partitioning and fast heuristics. The initial focus of my work has been neuron segmentation in electron micrscopy (D), for which I have developed methods that scale to multi terayte volumes and can incorporate biological priors. These methods have been used in various life science applications: building a high resolution genetic and morphological atlas of P. dumerilii (A), analyzing the morphology of precursor neural cells in sponges (B) or developing an imaging based SARS-CoV-2 antibody assay (C). I plan to continue this work and make these methods available through easy-to-use tools in order to democratize the access to large-scale volumetric segmentation.

I am dedicated to open source and open science and am actively contributing to several related efforts. In particular, the modelzoo, a resource to share deep learning models for microscopy image analysis and ome.ngff, a new image data format that supports efficient storage of large data and on-demand access in the cloud. Furthermore, I co-develop MoBIE, a Fiji plugin for exploring and sharing large multi-modal image data.


I give a lecture on deep learning for computer vision and hold seminars on applications of deep learning and machine learning in biology and medicine. Please check UniVZ for my current courses.


  • 2010-2013 Bachelor of Science, Physics, Ruprecht-Karls University Heidelberg
  • 2013-2016 Master of Science, Physics, Ruprecht-Karls University Heidelberg
  • 2016-2021 PhD, Physics, Ruprecht-Karls University Heidelberg
    • 2017-2018 Visiting scientist, Janelia Research Campus
    • 2018-2021 Visiting scientist, EMBL Heidelberg
  • 2021-2022 PostDoctoral Fellow, EMBL Heidelberg
  • 2022- Juniorprofessor, Georg-August University Goettingen
Plain Academic