Volumetric vision / segmentation

Cardiac MRI segmentation.

A notebook-led experiment adapting hierarchical transformer representations to three-dimensional anatomy and voxel-level masks.

Interactive architecture

A hierarchy that returns to voxel space.

Overlapping 3D patches move through four scales, then contribute together to the segmentation decoder.

Case study

Carrying context through a volume

01 / Problem

Do not treat slices as islands

Cardiac structures extend across the scan, and independently segmented slices can lose the geometry that makes the anatomy coherent.

02 / System

Build a multi-scale volume

Overlapping 3D patch embeddings feed a MixTransformer-style hierarchy whose multiple resolutions are decoded into voxel predictions.

03 / Artifact

Keep experimentation visible

The public repository is notebook-led, covering data loading, segmentation inspection, model construction, training experiments, and checkpoints.

Architecture detail

From volume to mask

  1. Load the volume

    Organize scan slices and target masks as a coherent three-dimensional sample.

  2. Tokenize in 3D

    Use overlapping patches so boundaries do not vanish at rigid token edges.

  3. Expand context

    Move through hierarchical stages that trade spatial resolution for broader semantics.

  4. Decode scales

    Fuse features from each stage and recover a dense voxel-level segmentation.

Evidence boundary

An implementation experiment, not a clinical system.

The notebooks document an architectural exploration and training workflow. They do not establish prospective clinical performance, deployment readiness, or medical-device validation.