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Research Projects

Projective Spatial Transformers & Registration

- MICCAI2020
- Oral Video
- Github

Brief Intro: We propose a novel Projective Spatial Transformer module that generalizes spatial transformers to projective geometry, thus enabling differentiable volume rendering. We demonstrate the usefulness of this architecture on the example of 2D/3D registration between radiographs and CT scans. Specifically, we show that our transformer enables end-to-end learning of an image processing and projection model that approximates an image similarity function that is convex with respect to the pose parameters, and can thus be optimized effectively using conventional gradient descent.

Robot-Assisted Femoroplasty

- TMRB paper
- SPIE2020 paper
- Oral slide
- Recorded oral presentation

Brief Intro: Femroplasty is a proposed therapeutic method for preventing osteoporotic hip fractures in elderly. Patient-specific femoroplasty requires accurate 3D pose estimation of the proximal femur and injection device positioning. We proposed a fiducial-free 2D/3D registration method for robot-assisted femoroplasty system navigation.

Dexterous Surgical Manipulator Segmentation and Localization

- IPCAI 2019 paper
- Oral slide
- Recorded oral presentation

Brief Intro: Continuum dexterous manipulators (CDMs), commonly referred to as snake-like robots, have demonstrated great premise for minimmally-invasive procedures. One key challenge of using CDMs is performing precise intra-operative control guided by a pre-operative patient-specific plan. We presented a method that leverages semantic information of the fluoroscopy imaged object to initialize 2D/3D registration within the capture range of image-based registration by performing concurrent segmentation and localization of the CDM in X-ray images.

Surgical Force Prediction

- MICCAI 2018 CARE workshop paper - Best Paper Award! (second place)
- Oral slide
- Github

Brief Intro: Robotic surgery has been proven to offer clear advantages during surgical procedures, however, one of the major limitations is obtaining haptic feedback. Since it is often challenging to devise a hardware solution with accurate force feedback, we propose the use of “visual cues” to infer forces from tissue deformation. We employ deep learning to infer forces from video as an attractive low-cost and accurate alternative to typically complex and expensive hardware solutions with RGB-Point Cloud Temporoal Convolutional Networks.



  • Gao, C., Liu, X., Gu, W., Armand, M., Taylor, R., Unberath, M. (2020) Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration. International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020.
    [arxiv preprint]
  • Gao, C., Grupp, R., Unberath, M., Taylor, R., Armand, M. (2020) Fiducial-Free 2D/3D Registration of the Proximal Femur for Robot-Assisted Femoroplasty. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE2020. Oral.
    [bibtex] [publisher] [pdf] [slide] [video recordings]
  • Gu., W, Gao, C., Grupp, R., Fotouhi, J., Thies, M., Navab, N., Armand, M., Unberath, M., (2020) Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients. MIML2020.
  • Zaech, J.N., Gao, C., Bier, B., Taylor, R.H., Maier, A., Navab, N. and Unberath, M. (2019) Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories. International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019. Oral, NIH Travel Award!.
    [bibtex] [publisher] [pdf]
  • Gao, C.*, Unberath, M.*, Taylor, R.H. and Armand, M. (2019) Localizing dexterous surgical tools in X-ray for image-based navigation. Information Processing in Computer-Assisted Interventions, IPCAI 2019. Oral.
    [bibtex] [publisher] [pdf] [slide] [poster] [video recordings]
  • Gao, C., Liu, X., Peven, M., Unberath, M., and Reiter, A. (2018) Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks. Computer Assisted and Robotic Endoscopy, MICCAI workshop 2018. Oral, Best Paper Award! (second place).
    [bibtex] [publisher] [pdf] [ slide]
  • Alambeigi, F., Wang, Y., Sefati, S., Gao, C., Murphy, R.J., Iordachita, I., Taylor, R.H., Khanuja, H. and Armand, M. (2017) A curved-drilling approach in core decompression of the femoral head osteonecrosis using a continuum manipulator. IEEE Robotics and Automation Letters, ICRA 2017.
    [bibtex] [publisher] [pdf]
  • Du, H., Ouyang, M., Gao, C., Hong, B., Yang, H., Wang, Y. and Huang, H. (2016) Line Propagation Based On FDT Probabilistic Tracking (LP-FPT) Organization for Human Brian Mapping Poster, OHBM 2016. [pdf]
  • Shen, W., Wang, B., Feng, J., Gao, C. and Ma, J., 2015, May. Differential CSIT acquisition based on compressive sensing for FDD massive MIMO systems. In 2015 IEEE 81st Vehicular Technology Conference, VTC Spring. [pdf]


  • Gao, C., Farvardin, A., Grupp, R., Bakhtiarinejad, M., Ma, L., Thies, M., Unberath, M., Taylor, R., Armand, M. (2020) Fiducial-Free 2D/3D Registration for Robot-Assisted Femoroplasty IEEE Transactions on Medical Robotics and Bionics, vol. 2, no. 3, pp. 437-446, Aug. 2020, doi: 10.1109/TMRB.2020.3012460.
    [bibtex] [publisher]
  • Sefati, S., Gao, C., Iordachita, I., Taylor, R.H. and Armand, M. (2020) Data-Driven Shape Sensing of Continuum Manipulators in Constrained Spaces Via Deep Neural Networks Using Fiber Bragg Grating. IEEE Sensors Journal (2020).
    [bibtex] [publisher]
  • Thies, M.*, Zaech, J.N.*, Gao, C., Taylor, R.H., Navab, N., Maier, A. and Unberath, M. (2020) A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance. Int J CARS (2020). Special Issue Best Paper Nominee
    [bibtex] [publisher]
  • Grupp, R., Unberath, M., Gao, C., Hegeman, R., Murphy, R., Alenxander, C., Otake, Y., McArthur, B., Armand, M., Taylor, R. (2020) Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration. Int J CARS 15, 759–769 (2020). IPCAI2020 Special Issue
    [bibtex] [publisher] [pdf]
  • Unberath, M.*, Zaech, J.N.*, Gao, C.*, Bier, B., Goldmann, F., Lee, S.C., Fotouhi, J., Taylor, R.H., Armand, M. and Navab, N. (2019) Enabling Machine Learning in X-ray-based Procedures via Realistic Simulation of Image Formation International journal of computer assisted radiology and surgery, IJCARS 2019. Invited Special Issue
    [bibtex] [publisher] [pdf]
  • Chen, F., Zhao, Z., Gao, C., Liu, J., Su, X., Zhao, J., Tang, P. and Liao, H. (2017) Clustering of morphological features for identifying femur cavity subtypes with difficulties of intramedullary nail implantation. IEEE journal of biomedical and health informatics, 22(4), pp.1209-1217.
    [bibtex] [publisher] [pdf]

Peer Review