| --- |
| license: other |
| task_categories: |
| - image-to-image |
| - image-segmentation |
| tags: |
| - medical-imaging |
| - brats |
| - synthrad |
| - amos |
| - chaos |
| - mri |
| - ct |
| - in-context |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # MVG_extract — pre-extracted 2D slice tiles for the MVG_Multimodal medical baseline |
|
|
| Canonical **256×256** 2D slice tiles for the four datasets used by the multimodal |
| [MVG_Multimodal](https://github.com/ChicagoPark/MVG_Multimodal) in-context image→image model |
| (cross-modal **translation** + binary **segmentation**): **BraTS2023, SynthRAD2023, AMOS22, CHAOS**. |
|
|
| These are the exact model inputs — extracting them once removes the per-sample full-volume load |
| (AMOS volumes are ~200 MB) so training/eval is compute-bound, and they are the **canonical bytes** |
| the fair-evaluation contract hashes (so any method consuming them is byte-comparable). |
|
|
| ## Contents |
|
|
| Everything unzips to a single `MVG_extract/` directory: |
|
|
| ``` |
| MVG_extract/{dataset}/{split}/{subject}__{img|mask}__{key}.npz |
| ``` |
| - `{dataset}` ∈ `brats, synthrad, amos, chaos` |
| - `{split}` ∈ `train, val, test` (BraTS has train/val only — its test uses the nnUNet/paired-PNG pools) |
| - `{key}` = modality (`t1n,t1c,t2w,t2f` / `mr,ct` / `ct,mri` / `t1dual,t2spir`) or `seg`/liver mask |
| - each `.npz` holds `zs` (int32, `n` slice indices) + `tiles` (uint8, `(n, 256, 256)`) |
|
|
| | dataset | train | val | test | modalities | task | |
| |---|--:|--:|--:|---|---| |
| | brats | 5752 | 307 | — | t1n,t1c,t2w,t2f (+seg) | translation + tumor seg | |
| | synthrad | 576 | 32 | 112 | mr, ct (brain/pelvis) | MR↔CT translation | |
| | amos | 580 | 32 | 108 | ct, mri (+liver) | liver seg | |
| | chaos | 90 | 12 | 18 | ct, t1dual, t2spir (+liver) | liver seg | |
|
|
| *(counts are `.npz` stacks; ~7,619 total, 9.3 GB.)* |
|
|
| ## Usage |
|
|
| ```python |
| import numpy as np |
| d = np.load("MVG_extract/synthrad/test/brain__BA211__img__mr.npz") |
| zs, tiles = d["zs"], d["tiles"] # (n,), (n,256,256) uint8 |
| img01 = tiles[0].astype(np.float32) / 255.0 # image -> [0,1] |
| # mask stacks (…__mask__…) are already {0,1}: gt = tiles[0].astype(np.float32) |
| ``` |
| Point the loader at the unzipped root: `MedicalProvider(name, split, 256, extract_root="…/MVG_extract")`. |
|
|
| ## Provenance |
|
|
| Rendered by `mm_extract.py` via the vendored `mm_preprocess` recipe (LPS reorient; per-volume body |
| bbox +15% → pad-square → resize 256; MR non-zero (1,99)-pct → CT HU window (−1000,1000); MR & CT share |
| the CT bbox). Frozen subject splits from |
| [`latent-diffusion_Multimodal/ldm/data/medical_splits`](https://github.com/ChicagoPark/MVG_Multimodal) |
| (seed 0). uint8 8-bit quantization is byte-consistent with the medical-imaging PNG pipeline. |
|
|
| Code + fair-evaluation protocol: **https://github.com/ChicagoPark/MVG_Multimodal** |
| |
