Erik Bjørnager Dam

Erik Bjørnager Dam

Professor

Member of:

    ORCID: 0000-0002-8888-2524

    Publication year:
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    1 - 10 out of 53Page size: 10
    1. 2023
    2. Published

      Towards Automatic Cartilage Quantification in Clinical Trials – Continuing from the 2019 IWOAI Knee Segmentation Challenge

      Dam, Erik Bjørnager, Desai, A. D., Deniz, C. M., Rajamohan, H. R., Regatte, R., Iriondo, C., Pedoia, V., Majumdar, S., Perslev, Mathias, Igel, Christian, Pai, A., Gaj, S., Yang, M., Nakamura, K., Li, X., Maqbool, H., Irmakci, I., Song, S., Bagci, U., Hargreaves, B., Gold, G. & Chaudhari, A., Feb 2023, In: Osteoarthritis Imaging.

      Research output: Contribution to journal › Journal article › peer-review

    3. E-pub ahead of print

      Multi‐planar 3D knee MRI segmentation via UNet inspired architectures

      Sengar, Sandeep Singh, Meulengracht, C., Boesen, Mikael Ploug, Overgaard, A. F., Gudbergsen, Henrik Rindel, Nybing, J. D., Perslev, Mathias & Dam, Erik Bjørnager, 2023, (E-pub ahead of print) In: International Journal of Imaging Systems and Technology. p. 1-14

      Research output: Contribution to journal › Journal article › peer-review

    4. Published

      The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

      Hirvasniemi, J., Runhaar, J., van der Heijden, R. A., Zokaeinikoo, M., Yang, M., Li, X., Tan, J., Rajamohan, H. R., Zhou, Y., Deniz, C. M., Caliva, F., Iriondo, C., Lee, J. J., Liu, F., Martinez, A. M., Namiri, N., Pedoia, V., Panfilov, E., Bayramoglu, N., Nguyen, H. H., Nieminen, M. T., Saarakkala, S., Tiulpin, A., Lin, E., Li, A., Li, V., Dam, Erik Bjørnager, Chaudhari, A. S., Kijowski, R., Bierma-Zeinstra, S., Oei, E. H. G. & Klein, S., 2023, In: Osteoarthritis and Cartilage. 31, p. 115-125

      Research output: Contribution to journal › Journal article › peer-review

    5. Published

      The Liver Tumor Segmentation Benchmark (LiTS)

      Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., Lohöfer, F., Holch, J. W., Sommer, W., Hofmann, F., Hostettler, A., Lev-Cohain, N., Drozdzal, M., Amitai, M. M., Vivanti, R., Sosna, J., Ezhov, I., Sekuboyina, A., Navarro, F., Kofler, F., Paetzold, J. C., Shit, S., Hu, X., Lipková, J., Rempfler, M., Piraud, M., Kirschke, J., Wiestler, B., Zhang, Z., Hülsemeyer, C., Beetz, M., Ettlinger, F., Antonelli, M., Bae, W., Bellver, M., Bi, L., Chen, H., Chlebus, G., Dam, Erik Bjørnager, Dou, Q., Fu, C., Georgescu, B., Giró-I-Nieto, X., Gruen, F., Han, X., Heng, P., Hesser, J., Moltz, J. H., Igel, Christian, Isensee, F., Jäger, P., Jia, F., Kaluva, K. C., Khened, M., Kim, I., Kim, J., Kim, S., Kohl, S., Konopczynski, T., Kori, A., Krishnamurthi, G., Li, F., Li, H., Li, J., Li, X., Lowengrub, J., Ma, J., Maier-Hein, K., Maninis, K., Meine, H., Merhof, D., Pai, A., Perslev, Mathias, Petersen, J., Pont-Tuset, J., Qi, J., Qi, X., Rippel, O., Roth, K., Sarasua, I., Schenk, A., Shen, Z., Torres, J., Wachinger, C., Wang, C., Weninger, L., Wu, J., Xu, D., Yang, X., Yu, S. C., Yuan, Y., Yue, M., Zhang, L., Cardoso, J., Bakas, S., Braren, R., Heinemann, V., Pal, C., Tang, A., Kadoury, S., Soler, L., van Ginneken, B., Greenspan, H., Joskowicz, L. & Menze, B., 2023, In: Medical Image Analysis. 84, 24 p., 102680.

      Research output: Contribution to journal › Journal article › peer-review

    6. 2022
    7. Published

      Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis

      Selvan, Raghav, Bhagwat, N., Anthony, L. F. W., Kanding, Benjamin Michael & Dam, Erik Bjørnager, 2022, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V. Springer, p. 506–516 (Lecture Notes in Computer Science, Vol. 13435).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    8. Published

      Conventional MRI-derived subchondral trabecular biomarkers and their association with knee cartilage volume loss as early as 1 year: a longitudinal analysis from Osteoarthritis Initiative

      Pishgar, F., Ashraf-Ganjouei, A., Dolatshahi, M., Guermazi, A., Zikria, B., Cao, X., Wan, M., Roemer, F. W., Dam, Erik Bjørnager & Demehri, S., 2022, In: Skeletal Radiology. 51, p. 1959–1966

      Research output: Contribution to journal › Journal article › peer-review

    9. Published

      Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets

      Perslev, Mathias, Pai, A., Runhaar, J., Igel, Christian & Dam, Erik Bjørnager, 2022, In: Journal of Magnetic Resonance Imaging. 55, 2, p. 1650-1663

      Research output: Contribution to journal › Journal article › peer-review

    10. Published

      Multivariable modeling of biomarker data from the phase 1 Foundation for the NIH Osteoarthritis Biomarkers Consortium

      Hunter, D. J., Deveza, L. A., Collins, J. E., Losina, E., Nevitt, M. C., Roemer, F. W., Guermazi, A., Bowes, M. A., Dam, Erik Bjørnager, Eckstein, F., Lynch, J. A., Katz, J. N., Kwoh, C. K., Hoffmann, S. & Kraus, V. B., 2022, In: Arthritis Care & Research. 74, 7, p. 1142-1153

      Research output: Contribution to journal › Journal article › peer-review

    11. 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 journal › Journal article › peer-review

    12. 2021
    13. Published

      Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning

      Tummala, S., Thadikemalla, V. S. G., Kreilkamp, B. A. K., Dam, Erik Bjørnager & Focke, N. K., 2021, In: Computers in Biology and Medicine. 139, 104997.

      Research output: Contribution to journal › Journal article › peer-review

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    ID: 176815263