From brain scans to an explorable 3D view.

I wanted to understand the images while I waited for answers.

The project began during a personal health issue. I had brain-scan images on disk and was waiting for professional answers. Having the images did not mean I had an approachable way to explore them. I wanted to make the data easier to see and understand for myself during that wait.

The starting material was a sequence of individual slices. Each image showed one part of a larger structure, but the connection between those images was something I wanted to experience spatially. My question became a software problem: how could I turn the slices into a view I could move through?

I brought together Python development, ChatGPT-assisted problem solving, and Blender to work on that question. The motivation was personal access to a clearer visualization while I awaited the professional report. It gave the project a concrete purpose and shaped the emphasis on navigating inside the result.

Start with the slices. Build the visual continuity between them.

I built a Python tool that read the brain-image slices from disk and prepared them for a three-dimensional workflow. The input was the scan material I already had. The aim was to make those separate images useful together inside a creative 3D environment.

The gaps between slices were an important part of the problem. Simply placing the original images next to one another would leave the viewing experience limited by the separation between them. I used generative AI to interpolate intermediate images and add visual continuity across those gaps.

Those intermediate images extended the sequence that would become the Blender stack. They were estimates between the original slices, giving the presentation more steps to move through. That distinction is part of the method: the scan images supplied the starting information, while the interpolation supplied the additional visual transitions.

This joined data handling with image generation in one workflow. Reading the files was the entry point; preparing a sequence that could be assembled and explored was the actual product of this stage.

Empty image space should not block the view.

Stacking image rectangles is not enough to create a useful internal view. The background around the scan content can become a series of opaque surfaces, hiding the structure behind it. Before assembling the images, I needed to deal with that negative space.

I used image processing to remove the backgrounds and turn them into transparency. The intent was to carry the visible scan content into the stack while allowing the empty parts of each image to disappear from the presentation.

That changed what the stack could communicate. Instead of encountering a succession of solid image backgrounds, the viewer could look through the transparent regions and follow the forms across layers. Background removal was therefore part of the spatial design, not just a cosmetic cleanup step.

Assemble a view you can enter and move through.

The workflow brought the original slices and generated intermediate images into Blender as a stack. With background transparency in place, the layers formed a navigable representation of the brain-image data. The point was to explore internal relationships, rather than only produce an attractive exterior view.

Blender gave me a familiar environment for moving around the result and choosing how to present it. I could make the relationship between slices part of a visual experience and use different viewpoints to explain what the assembled structure looked like.

The original demonstration shows multiple views, colored scan structures, and side-by-side presentation inside Blender. These choices helped make the spatial result readable. A second view can explain something that one angle hides, while color and composition help the viewer stay oriented.

This is where the technical and creative sides of the project met. The processing pipeline made the material available in a new form; the Blender presentation made that form something another person could follow.

Move from a layered view toward a modeled volume.

I later added a voxel-based algorithm to create an actual three-dimensional model. This extended the work beyond the initial arrangement of image slices and interpolations. The project now had two related forms: a navigable image stack and a model built through a volumetric representation.

That progression mattered to the direction of the tool. The stacked view made the scan sequence tangible, while the later modeling step explored how the information could become a three-dimensional object in the workflow.

I see those as successive stages of the same experiment. First, make the images accessible as space. Then develop that spatial representation into a model. Keeping the purpose of each stage clear made the overall project easier to explain.

A personal tool, shared for other people to explore.

I made the original tool available as a free download. What began as a way to engage with my own scan images became something I could share: a workflow connecting Python, AI-assisted image interpolation, transparency, Blender, and later voxel modeling.

I published the demonstration on November 29, 2024 through the keiazo and Asaday Instagram accounts. It shows the visual result inside Blender and the combination of technical development and creative presentation that drove the work.

The most useful lesson was that access to information and access to a usable representation are different problems. I already had the images. Building the tool gave me another way to engage with them while waiting for professional answers, and sharing it turned a personal experiment into a public project.

The project also reinforced how much the presentation matters. Reading files, filling gaps, and removing backgrounds were necessary steps, but the goal was the experience they enabled: moving through a complex structure and seeing how its parts relate.

Watch the original November 2024 demonstration →