Peidi Xu

Peidi Xu

Ph.d.-stipendiat


Udgivelsesår:
  1. 2023
  2. Udgivet

    A hybrid approach to full-scale reconstruction of renal arterial network

    Xu, Peidi, von Holstein-Rathlou, Niels-Henrik, Søgaard, S. B., Gundlach, C., Sørensen, Charlotte Mehlin, Erleben, Kenny, Sosnovtseva, Olga & Darkner, Sune, 9 maj 2023, I: Scientific Reports. 13, 1, 15 s., 7569.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  3. Udgivet

    Deep-learning-based segmentation of individual tooth and bone with periodontal ligament interface details for simulation purposes

    Xu, Peidi, Gholamalizadeh, T., Moshfeghifar, Faezeh, Darkner, Sune & Erleben, Kenny, 2023, I: IEEE Access. 11, s. 102460-102470

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  4. Udgivet

    Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation

    Xu, Peidi, Lee, B., Sosnovtseva, Olga, Sørensen, Charlotte Mehlin, Erleben, Kenny & Darkner, Sune, 2023, Medical Image Learning with Limited and Noisy Data - 2nd International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Proceedings. Xue, Z., Antani, S., Zamzmi, G., Yang, F., Rajaraman, S., Liang, Z., Huang, S. X. & Linguraru, M. G. (red.). Springer, s. 191-201 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14307 LNCS).

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  5. 2022
  6. Udgivet

    Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning

    Xu, Peidi, Moshfeghifar, Faezeh, Gholamalizadeh, T., Nielsen, Michael Bachmann, Erleben, Kenny & Darkner, Sune, 2022, Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022 Held in Conjunction with MICCAI 2022 Singapore, September 22, 2022 Proceedings. Zamzmi, G., Antani, S., Rajaraman, S., Xue, Z., Bagci, U. & Linguraru, M. G. (red.). Springer Science and Business Media Deutschland GmbH, s. 153-162 10 s. (Medical Image Learning with Limited and Noisy Data, Bind 13559).

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

ID: 226561698