SciKit-Surgery libraries implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation.

Getting started:

Wondering which library is suitable for your job and how to use it? Check out the list of included libraries, relevant documentation and demo tutorials.

Packages:

Library Purpose
scikit-surgerycore Algorithms/tools common to all scikit-surgery packages. Read more…
scikit-surgeryimage Image processing algorithms using OpenCV. Read more…
scikit-surgeryvtk Implements VTK functionality for IGS applications. Read more…
scikit-surgeryutils Example applications/utilities. Read more…
scikit-surgerycalibration Calibration algorithms (camera/pointer/ultrasound etc). Read more…
scikit-surgerysurfacematch Stereo reconstruction and point cloud matching. Read more…
scikit-surgerytf IGS models implemented in TensorFlow. Read more…
scikit-surgerytorch IGS models implemented in PyTorch. Read more…
scikit-surgerynditracker Interface for Northern Digital (NDI) trackers. Vicra, Spectra, Vega, Aurora. Read more…
scikit-surgeryarucotracker Interface for OpenCV ARuCo. Read more…
scikit-surgeryspeech Speech/Wakeword detection
scikit-surgerydocker Automate containerization of project/algorithm using docker.
scikit-surgerychallenge Automate downloading, execution and results evaluation of submitted docker.

Publications: