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SciKit-Surgery is a collection of compact libraries developed for surgical navigation. Individual libraries can be combined using Python to create clinical applications for translational research.
SciKit-Surgery is a collection of compact libraries developed for surgical navigation. Individual libraries can be combined using Python to create clinical applications for translational research.
SciKit-Surgery 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.
Step 4: Use the Python Template to create your new project. This tutorial uses a sphere fitting algorithm as an example case, as it strikes a nice balance between simplicity and usefulness. Fitting models to data is a key part of medical image computing, so hopefully the user can see how their own algorithms could be inserted into the software ...
The SciKit-Surgery Tutorial on Software Development for Clinical Translation is part of the SciKit-Surgery software project, developed at the Wellcome EPSRC Centre for Interventional and Surgical Sciences, part of University College London (UCL). This SciKit-Surgery Tutorial is tested on Python 3.8. and may support other Python versions ...
The SciKit-Surgery package scikit-surgeryutils simplifies the process by integrating QT (PySide2), OpenCV, and VTK into a simple to use Python library. This tutorial will guide the user in creating an augmented reality application in around 70 lines of code.
The SciKit-Surgery Python template has already populated this with a couple of example algorithms, addition and multiplication. These are nice examples, but a bit too simple for our tutorial. So lets delete them and create our own file, sphere_fitting.py.
