Roxane Licandro

Contact

      Dipl.-IngRoxane Licandro. Roxane Licandro, BSc
      Email: roxane.licandro @ meduniwien.ac.at
      Phone: +43(0) 1 40400 73724

      Computational Imaging Research Lab
      Department of Biomedical Imaging and Image-guided Therapy
      Medical University of Vienna
      Waehringer Guertel 18-20
      A-1090 Vienna / Austria


Office
Anna Spiegel Center of Translational Research
(building 25, floor 7, room 27)

I am also affiliated with the Computer Vision Lab at TU Wien.

News and Upcoming

pippi logo simple

 We are glad to announce that the 5th Perinatal Imaging, Placental and Preterm Image analysis workshop (PIPPI 2020) will be held again in conjunction with MICCAI 2020 virtually, October 2020.

 

Looking forward to be a speaker at Ultrasound meets Magnetic Resonance taking place in Vienna from 10th - 12th September 2020.

Our work on "Evolution Risk Prediction in Multiple Myeloma" got accepted at ECR 2020, 15th - 19th July 2020, Vienna.

Looking forward to be a speaker at the Summer School on Image Processing (SSIP 2020) in Szeged Hungary, 9th - 18th July 2020.

"Fake New World: Deep Fakes, Deep Learning, Künstliche Intelligenz", deep learning in medicine performed at CIR broadcasted on TV: ORF 1 Magazine Newton, 30.05.2020 18:15 - Check it out (time code 19:09) Link

Our book "Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis" is now available via Springer Link.

Research interests

  • Spatio-temporal modelling
  • Diffeomorphic registration
  • Fetal and pediatric brain development
  • Functional brain networks and plasticity
  • Automatic MRD Assessment in Leukaemia

Short CV

Roxane Licandro graduated the master study of medical informatics at the Vienna University of Technology (TU Wien) with distinction in January 2016.  She worked as study assistant and teaching assistant at the Pattern Recognition and Image Processing (PRIP) Group,  and as teaching assistant at the  Institute of Computer Graphics and Algorithms and the Computer Vision Lab at TU Wien. She joined the CIR-Lab in summer 2012 and wrote her master’s thesis in cooperation with CVL and the CIR-Lab. She is currently engaged in her PhD studies at TU Wien in cooperation with CIR.

Projects

Awards & Achievements

  • Team Nomination, Best Lecture Award 2019 - TU Wien
  • Marie Curie Alumni Association Micro Travel Grant, August 2018
  • Best Paper Award at the International Conference on Clinical and Medical Image Analysis ICCMIA'18, July 2018
  • February 2017 – July 2017 Marie Skłodowska Curie Fellowship within the European Project AutoFLOW
  • Best Poster Award – 3rd Austrian Biomarker Symposium on Early Diagnostics 2016 (3rd Place)

Scientific Activities

Selected Recent Publications

Licandro R., Hofmanninger J., Perkonigg M., Röhrich S., Weber M.-A., Wennmann M., Kintzele L., Piraud M., Menze B., Langs G., "Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma", International Conference on Medical Imaging with Deep Learning (MIDL), London, July 2019. https://arxiv.org/abs/1907.13539.

Licandro R. and Schlegl T., Reiter M., Diem M., Dworzak M., Schumich A., Langs G., Kampel M., "WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia, 24th International Conference on Pattern Recognition (ICPR) 2018, Beijing, August 2018.

Licandro R. Nenning K.H., Schwartz E., Kollndorfer K., Bartha-Doering L., Liu H., Langs G. "Assessing Reorganisation of Functional Connectivity in the Infant Brain". In: Cardoso M. et al. (eds) Fetal, Infant and Ophthalmic Medical Image Analysis. MICCAI FIFI 2017, OMIA 2017. Lecture Notes in Computer Science, vol 10554. Springer, Cham. Quebéc (Canada), September 2017.PDF

Licandro R., Langs G., Kasprian G., Sablatnig R. Prayer D., Schwartz E., " Longitudinal Atlas Learning for Fetal Brain Tissue Labeling using Geodesic Regression", WiCV Workshop at the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas (U.S.), July 2016.

Thesis

R. Licandro, "Longitudinal Diffeomorphic Fetal Brain Atlas Learning for Tissue Labeling using Geodesic Regression and Graph Cuts", Vienna University of Technology, January 2016. Link