See also the instructions for applying here.
All the current ideas for GSoC 2024 are listed bellow:
Use tools like unittest or pytest to add tests to InVesalius code. That way, when adding new features or bug fixes, you can verify that no previous code has been affected. Also run this code when committing and sending pull requests on Github.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies.
Deliverables:
- Unit tests in InVesalius code
- Github-CI action for running tests when committing or posting a pull-request
Programming Languages: Python and YAML
Duration: 175h
Difficulty level: Intermediate
Mentor: Thiago Franco de Moraes - totonixsame@gmail.com
References: invesalius/invesalius3#496 and https://realpython.com/python-testing/
Since python 3.5 it is possible to add type information to the parameters of functions, methods and classes. With this information it is possible to use tools like Mypy to catch calls made with the wrong type. This way you can avoid errors and make your code easier to maintain and grow
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies.
Deliverables:
- Type information added to InVesalius functions, methods and classes.
- Github-CI action for catching type errors using tools like Mypy
Programming Languages: Python and YAML
Duration: 175h
Difficulty level: Intermediate
Mentor: Thiago Franco de Moraes - totonixsame@gmail.com
References: invesalius/invesalius3#497, https://docs.python.org/3/library/typing.html and https://mypy-lang.org/
Tool that allows the user to activate the capture of logs and errors. It should be possible to save the sequence of events in a text file. It will be necessary to add logging capture to all functions and functionalities and their respective levels (debug, info, warn, error or critical).
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies.
Deliverables:
- Graphic interface integrated to InVesalius that allows the user to activate the tool and save the logs.
- Log support to all InVesalius functions and functionalities.
Programming Languages: Python
Duration: 350h
Difficulty level: Hard
Mentor: Paulo Henrique Junqueira Amorim - paulojamorim@gmail.com
References: invesalius/invesalius3#498, https://docs.python.org/3/library/logging.html and https://realpython.com/python-logging/
Currently, InVesalius has fixed scene lighting options. Shaders allow you to manipulate part of the graphics pipeline, being able, for example, to change the color, lighting and camera of the scene. In the medical field this is useful as some regions can be better highlighted just by changing the lighting.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background of computer graphics.
Deliverables:
- Tool that allows loading and applying custom shaders in InVesalius rendering scenes. It is desirable that the tool displays the shader code and allows editing and applying for the user to see the changes.
Programming Languages: Python and GLSL
Duration: 175h
Difficulty level: Intermediate
Mentor: Paulo Henrique Junqueira Amorim - paulojamorim@gmail.com
References: invesalius/invesalius3#499, https://vtk.org/Wiki/Shaders_In_VTK and https://learnopengl.com/Getting-started/Shaders
The neuronavigation feature of InVesalius currently does not follow user experience (UX) principles in the user interface and workflow design. The software usability can be significantly improved by applying the basic UX design principles.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies.
Deliverables:
- Description and documentation of the neuronavigation workflow
- Refactored neuronavigation user controls and settings
Programming Languages: Python
Duration: 350h
Difficulty level: Hard
Mentor: Victor Hugo Souza - vhosouza@gmail.com
References: invesalius/invesalius3#506
The real-time neuronavigation feature of InVesalius currently works based on multi-threading using Queues, Events, and Jobs (look at #242). Optimization (utilizing the least memory, minimizing its CPU time, and offering high speed) is needed to improve neuronavigation performance.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background of computer graphics.
Deliverables:
- Characterize step-by-step code execution time for neuronavigation
- Improve threads’ sleep times
- Investigate: WX GUI becomes slower when navigation is on
- Optimize 3D rendering/updating scene
Programming Languages: Python
Duration: 350h
Difficulty level: Hard
Mentor: Victor Hugo Souza - vhosouza@gmail.com
References: invesalius/invesalius3#529 and invesalius/invesalius3#242
User can edit a mask in InVesalius slice by slice. That is a very repetitive and error prone job. Since InVesalius has 3D visualizations, it's possible to draw over the rendering area and remove the voxels projected in the painted areas.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background of computer graphics.
Deliverables:
- Tool to edit masks using projections.
Programming Languages: Python
Duration: 350h
Difficulty level: Hard
Mentor: Thiago Franco de Moraes - totonixsame@gmail.com
References: invesalius/invesalius3#91 and https://en.wikipedia.org/wiki/3D_projection
Currently, the InVesalius interface is not properly designed for different modes (Light and Dark) mode, especially on MacOS, for example.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background of computer graphics.
Deliverables:
- Unify the color scheme to different modes (light and dark)
- Apply standard and default colors to UI components
- Adjust component sizing that are disrupted across platforms
- Introduce modern icons
Programming Languages: Python
Duration: 175h
Difficulty level: Intermediate
Mentor: Petrus Kirsten - petrus.kirsten@gmail.com / Victor Malheiro - victorhugomalheiro@gmail.com
The process of transcranial magnetic stimulation neuronavigation involves the generation of motor evoked potentials (MEP), which are detected and measured using electromyography. These MEP signals can be effectively represented through a color map projected onto the brain surface. This innovative approach enables real-time visualization of brain motor mapping, offering a dynamic and comprehensive understanding of neural activity during the procedure.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies.
Deliverables:
- Develop pipeline to receive motor mapping data using websocket
- Projection of motor mapping data, based on the neuronavigation position, in the brain surface
- GUI to define the color map preferences and customize the number of stimuli
- Coordinate communication between the websocket data receiver, projection module, and GUI.
Programming Languages: Python
Duration: 350h
Difficulty level: Hard
Mentor: Thais Marchetti - thaismarchetti123@gmail.com / Lucas Betioli - lucasantoniobetioli@gmail.com
Add texture to a surface based on the image tissue.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background of computer graphics.
Deliverables:
- Tool to set a texture to a surface.
Programming Languages: Python
Duration: 350h
Difficulty level: Hard
Mentor: Thiago Franco de Moraes - totonixsame@gmail.com
References: https://en.wikipedia.org/wiki/Texture_mapping
InVesalius uses Marching Cubes to extract surface from volumetric images. It's interesting to have other methods like Marching Tetrahedra and Dual Contouring.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background of computer graphics.
Deliverables:
- Tool to generate surfaces using other methods than Marching Cubes.
Programming Languages: Python and Cython
Duration: 350h
Difficulty level: Hard
Mentor: Thiago Franco de Moraes - totonixsame@gmail.com
References: https://0fps.net/2012/07/12/smooth-voxel-terrain-part-2/ https://en.wikipedia.org/wiki/Marching_tetrahedra https://en.wikipedia.org/wiki/Isosurface
When saving or loading files (except DICOM), InVesalius does not provide any feedback to the user. The goal of this task is to implement a window that displays the progress when loading or saving the different types of files that InVesalius handles. This task also includes replacing the progress bar of the volume rendering functionality.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies. Good background in GUI.
Deliverables:
- Windows with progress bar for the functionalities specified in the referenced issues (references).
Programming Languages: Python
Duration: 90h
Difficulty level: Easy
Mentor: Paulo Henrique Junqueira Amorim - paulojamorim@gmail.com
Github Actions is used for various softwares to run tests when a commit or pull request is submitted. Also, it can be used to compile software and create binaries ready to be used by the users. The idea in this task is to use Github Actions to create nightly and release InVesalius installers for Windows and macOS.
Requirements: Computer with Windows, Linux or Mac OS installed. A source code editor, Python language and InVesalius libraries dependencies.
Deliverables:
- Github action to create nightly and release installers for InVesalius for Windows and macOS.
Programming Languages: Python
Duration: 175h
Difficulty level: Intermediate
Mentor: Paulo Henrique Junqueira Amorim - paulojamorim@gmail.com and Thiago Franco de Moraes - totonixsame@gmail.com
References: https://docs.github.com/en/actions https://pyinstaller.org/en/stable/