Jens Petersen

Jens Petersen

Associate Professor


  1. Published

    Segmentation of roots in soil with U-Net

    Smith, Abraham George, Petersen, Jens, Selvan, Raghav & Rasmussen, Camilla Ruø, 2020, In: Plant Methods. 16, p. 1-15 13.

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Published

    RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

    Smith, Abraham George, Petersen, Jens, Terrones-Campos, C., Berthelsen, A. K., Forbes, N. J., Darkner, Sune, Specht, Lena & Vogelius, I. R., 2022, In: Medical Physics. 49, 1, p. 461-473

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Published

    RootPainter: deep learning segmentation of biological images with corrective annotation

    Smith, Abraham George, Han, Eusun, Petersen, Jens, Olsen, N. A. F., Giese, C., Athmann, M., Dresbøll, Dorte Bodin & Thorup-Kristensen, Kristian, 2022, In: New Phytologist. 236, p. 774-791 18 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Published

    Segmenting Two-Dimensional Structures with Strided Tensor Networks

    Selvan, Raghav, Dam, Erik Bjørnager & Petersen, Jens, 2021, Information Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings. Feragen, A., Sommer, S., Schnabel, J. & Nielsen, M. (eds.). Springer, p. 401-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12729 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  5. Published

    Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks

    Selvan, Raghav, Dam, Erik Bjørnager, Flensborg, S. A. & Petersen, Jens, 2022, In: The Journal of Machine Learning for Biomedical Imaging. 2022, p. 1-24 005.

    Research output: Contribution to journalJournal articleResearchpeer-review

  6. Published

    Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

    Selvan, Raghav, Kipf, T., Welling, M., Juarez, A. G., Pedersen, J. H., Petersen, Jens & de Bruijne, Marleen, 2020, In: Medical Image Analysis. 64, 12 p., 101751.

    Research output: Contribution to journalJournal articleResearchpeer-review

  7. Published

    Extracting tree structures in CT data by tracking multiple statistically ranked hypotheses

    Selvan, Raghav, Petersen, Jens, Pedersen, J. H. & de Bruijne, Marleen, 18 Oct 2019, In: Medical Physics. 46, 10, p. 4431-4440

    Research output: Contribution to journalJournal articleResearchpeer-review

  8. Published
  9. Published

    Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods

    Schön, Julian Elisha, Selvan, Raghav & Petersen, Jens, 2022, Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, p. 24-33 (Lecture Notes in Computer Science, Vol. 13609).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  10. Published

    Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing

    Selvan, Raghav, Petersen, Jens, Pedersen, J. J. H. & de Bruijne, Marleen, 2017, Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings. Cardoso, M. J., Arbel, T., Ferrante, E., Pennec, X., Dalca, A. V., Parisot, S., Joshi, S., Batmanghelich, N. K., Sotiras, A., Nielsen, M., Sabuncu, M. R., Fletcher, T., Shen, L., Durrleman, S. & Sommer, S. (eds.). Springer, p. 53-63 (Lecture notes in computer science, Vol. 10551).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

ID: 22733621