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Tuesday Oct. 13, 2020, 5 p.m.–Oct. 13, 2020, 5:30 p.m. in Jupyter in Scientific Research

SlicerJupyter: a 3D Slicer kernel for interactive publications

Jean-Christophe Fillion-Robin

Audience level:

Brief Summary

We present “SlicerJupyter”, a 3D Slicer kernel allowing developers to implement image processing and visualization workflows in a notebook. 3D Slicer is C++ desktop app including tools for DICOM interop, GPU accelerated volume viz, non-linear transformations, device interfaces, and other features from core analysis to full-scale specializations such as radiation therapy/neuroimaging/astronomy.


3D Slicer (or Slicer for short) is a C++ desktop application that uses Qt, ITK, and VTK libraries for visualization and medical image analysis. 3D Slicer contains a wide range of tools to handle research and clinical workflows such as DICOM interoperability, GPU accelerated volume visualization, non-linear transformations, device interfaces, and many other features ranging from core analysis primitives to full-scale specializations for domains such as radiation therapy, neuroimaging, and even astronomy.

Slicer’s embedded Python interpreter makes all its features accessible with the Python programming language. Slicer has a simple built-in console to run Python commands interactively and can run Python scripts from files, but these are not as convenient as cell-based interactive notebooks, which have become popular among data scientists and researchers in recent years.

In this talk, we present “SlicerJupyter”, a 3D Slicer kernel allowing developers to implement complete data processing workflows in a notebook, using the powerful medical imaging and bioimaging tools in 3D Slicer. We will describe how Jupyter interactive widgets (sliders, buttons, etc.) can be used to control Slicer, modify data, or adjust processing and visualization parameters.

We will review the different levels of interactivity: