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# SynthRAD2025 – Task 1 & Task 2 Solutions
[](https://grand-challenge.org/)
[](https://www.docker.com/)
[](LICENSE)
This repository contains our solutions for the **MICCAI Grand Challenge – SynthRAD2025**, focusing on **Task 1** and **Task 2**.
Our team achieved **1st place** in the **post-challenge leaderboard** for both tasks (with **Task 1 also ranking 1st during the official test phase**).
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## 🏆 Challenge Overview
- **Task 1**: MRI-to-CT synthesis (**MR → sCT**)
- **Task 2**: CBCT-to-CT synthesis (**CBCT → sCT**)
Our methods emphasize **robust image synthesis**, **reproducible pipelines**, and **multi-region generalization**.
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### File Descriptions
- **`docker_task_1/`**
Contains all necessary files to build and run the Docker image for **Task 1** (MR → sCT).
- `process.py`: Script that performs inference, converting MR images into synthetic CT (sCT).
- **`docker_task_2/`**
Contains all necessary files to build and run the Docker image for **Task 2** (CBCT → sCT).
- `process.py`: Script that performs inference, converting CBCT images into synthetic CT (sCT).
- **`Normalization Config Files`**
- `260_gt_nnUNetResEncUNetLPlans.json/540_gt_nnUNetResEncUNetLPlans.json`: Normalization configuration for the **Abdomen** region.
- `262_gt_nnUNetResEncUNetLPlans.json/542_gt_nnUNetResEncUNetLPlans.json`: Normalization configuration for the **Head & Neck** region.
- `264_gt_nnUNetResEncUNetLPlans.json/544_gt_nnUNetResEncUNetLPlans.json`: Normalization configuration for the **Thorax** region.
These files are essential for inverse normalization, ensuring that the synthesized CT intensities are mapped back to their correct clinical ranges.
- **`Dockerfile`**
Defines all steps and dependencies needed to build the Docker image. It ensures reproducibility and consistency across environments.
- **`base_algorithm/`**
Contains the baseline algorithm files provided by the official **SynthRAD2025 template**, serving as the foundation for our solution.
- **`build.sh`**
Shell script for automating the Docker build process.
- **`export.sh`**
Shell script for exporting the built Docker image into a compressed archive for submission or deployment.
- **`requirements.txt`**
Lists all Python dependencies required to run the code.
- **`revert_normalisation.py`**
Script to apply **inverse normalization** to synthesized CT outputs, restoring them to the correct intensity distributions for downstream evaluation.
## 🚀 Getting Started
- **`nnunet_results`**
Before starting inference, you also need to create a folder called `nnunet_results/` in docker_task_1/docker_task_2 and place your **trained models** under the `nnunet_results/` directory so that inference can correctly locate and load them.
### 1. Build the Docker Image
```bash
cd docker_task_1
bash build.sh
To test the algorithm locally, you can run the Docker container with GPU support, memory limit, and a larger shared memory (`/dev/shm`) size (e.g., 8 GB).
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