tan200224/Synthetic-CT-Scan_VAE_Conditional

VAE CT Slice Generator

Generate a single 2D CT slice from a segmentation mask using a VAE trained on 3D CT data. The model encodes your mask, decodes to 4 slices (3D volume), and returns slice 2 as a 2D image for the web.

How to use

1. 1.Upload a mask image — Grayscale segmentation mask (any size; it will be resized to 256×256).
2. 2.Click Run — The model encodes the mask, decodes to 4 slices, and shows slice 2.
3. 3.Download — Use the output image as needed.

The model expects a single grayscale mask. It is duplicated into 4 “slices” (matching training), then the VAE encodes → decodes to 4 slices; only slice index 2 is returned because the API cannot return 3D volumes.

API

POST /generate

Upload a mask image; get back a single slice as a data URI.

Request: multipart/form-data with a file (mask image).

Response:

{
  "image": "data:image/png;base64,..."
}

POST /predict

Upload a mask image; get back the slice as base64 PNG.

Request: multipart/form-data with a file (mask image).

Response:

{
  "image": "<base64 PNG string>",
  "slice_index": 2
}

Model & input/output

• —Model: VAE trained on 3D CT data (4-channel input/output, 256×256).
• —Input (training): Label/mask as 4 slices (1, 4, 256, 256). This Space accepts one grayscale mask, resizes to 256×256, normalizes to [0, 1], and duplicates it to 4 slices.
• —Output (model): decode(embedding) has shape (1, 4, 256, 256) (4 slices). This Space returns one slice (index 2) as a 2D PNG.

Environment (optional)

Set these in your Space Settings → Variables and secrets if you use a different repo or file.

Run locally

pip install -r requirements.txt
python app.py

Open http://localhost:7860 for the Gradio UI; use /generate or /predict for the API.