Learning to See Forces
Endoscopic video is a passive sensor that is freely available, in the sense that any minimally-invasive procedure already utilizes it. We employ deep learning to infer forces from video as an attractive low-cost and accurate alternative to typically complex and expensive hardware solutions. Our method results in a mean absolute error of 0.814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.
Detecting Dexterous Tool in X-ray Image
X-ray image based surgical tool navigation has received increasing interest since it is fast and supplies accurate images of deep seated structures. This work describes a first step to- wards leveraging semantic information of the imaged object to initialize 2D/3D registration within the capture range of image-based registration by performing concurrent segmentation and localization of the Continuous Dexterous Manipulator in X-ray images.
Dual Energy X-ray Material Decomposition
Standard X-ray imaging brings difficulty for surgeons to identify region of interest (ROI) features from anatomical clutter. Dual energy X-ray en- ables anatomical clutter reduction via material decomposition by utilizing the physical properties of X-ray formulations. We proposed a novel learning-based pipeline of doing dual energy X-ray material decomposition, by explicitly combining model-driven approach, physical constraints and learning-based regularization to an integrated optimization objective function. Poster