Supervision: Prof. Dr. Nassir Navab, Dr. Shadi Albarqouni, Dr. Seong Tae Kim, Dr. Guillaume Landry
We are looking for a student to support us in a joint project with our partners in the Department of Radiation Oncology of the University Hospital of the LMU Munich. This MSc project is intended for a student who will be available for 6 months. Please see the details of the master thesis:
2. Novel spatio-temporal segmentation with memory networks
If you are interested, please contact Prof. Guillaume Landry (guillaume.landry@med.uni-muenchen.de) and Dr. Shadi Albarqouni (shadi.albarqouni@tum.de) for details.
[1] Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
[2] Milletari, F., Navab, N. and Ahmadi, S.A., 2016, October. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV) (pp. 565-571). IEEE.
[3] Degel, M.A., Navab, N. and Albarqouni, S., 2018, September. Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 630-637). Springer, Cham.
[4] Oh, S.W., Lee, J.Y., Xu, N. and Kim, S.J., 2019. Video object segmentation using space-time memory networks. IEEE International Conference on Computer Vision (arXiv:1904.00607).
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Title: | Development of spatio-temporal segmentation model for tumor volume calculation in micro-CT |
Abstract: | To develop a spatio-temporal segmentation model where the network is exposed to previous temporal information and builds this complex mapping to segment a given mouse micro-CT image to allow accurate tumor volume calculations. A dataset with micro-CT scans of over 69 mice with repeat imaging is available with ground truth annotations. Mice were either treated with radiotherapy or left untreated. The small animal data act as a surrogate for clinical datasets treated with MR-linac technology, which requires automatic spatio-temporal segmentation. |
Student: | Tetiana Klymenko |
Director: | Prof. Dr. Nassir Navab |
Supervisor: | Dr. Shadi Albarqouni, Dr. Seong Tae Kim, Dr. Guillaume Landry |
Type: | Master Thesis |
Area: | Segmentation, Machine Learning, Medical Imaging |
Status: | finished |
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