CBCT/app.json

{
    "display_name": "Synthesis: CBCT",
    "short_description": "Supervised whole-body CBCT-to-sCT model for SynthRAD Task 2.<br><br><b>Training data:</b><br>929 paired CT-CBCT cases from SynthRAD 2023+2025.<br><br><b>How to cite:</b><br><cite>V. Boussot et al., <i>Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration</i>, arXiv preprint arXiv:2510.21358, <a href=\"https://doi.org/10.48550/arXiv.2510.21358\">https://doi.org/10.48550/arXiv.2510.21358</a>.</cite>",
    "description": "<b>Description:</b><br>Supervised whole-body CBCT-to-sCT model trained using SynthRAD 2023 and SynthRAD 2025 Task 2 datasets, distributed as five validation-fold checkpoints (<code>CV_0.pt</code> to <code>CV_4.pt</code>). Training pairs are aligned with IMPACTReg. The available Task 2 data include 291 patients from SynthRAD 2023 and 638 patients from SynthRAD 2025, for 929 patients in total.<br><br><b>Architecture and loss:</b><br>2.5D UNet++ with a ResNet34 encoder, trained with KonfAI and optimized using IMPACT-Synth, a perceptual loss leveraging semantic priors from <b>SAM 2.1-s</b>.<br><br><b>Training data:</b><br>Whole-body paired CT-CBCT volumes come from SynthRAD 2023 Task 2 (<code>brain</code>, <code>pelvis</code>) and SynthRAD 2025 Task 2 (<code>AB</code>, <code>HN</code>, <code>TH</code>). Additional OOD evaluation cohorts are available for <code>AB_ood</code>, <code>HN_ood</code>, and <code>TH_ood</code>. Pairs are aligned with IMPACTReg, and the associated transform files are available at <a href=\"https://huggingface.co/datasets/VBoussot/synthrad2023-impact-registration\">synthrad2023-impact-registration</a> and <a href=\"https://huggingface.co/datasets/VBoussot/synthrad2025-impact-registration\">synthrad2025-impact-registration</a>. The files <code>CrossValidation_0.txt</code> to <code>CrossValidation_4.txt</code> correspond to validation folds for the distributed checkpoints, and <code>Validation.txt</code> contains 150 release-validation patients.<br><br><b>Release performance (CV overall, n=150):</b><br>Dice 0.758, MAE 53.55 HU, SSIM 0.939, PSNR 32.09 dB, SAM 13.70, Reg 1.04, Uncertainty 784.24.<br><br><b>Inference note:</b><br>The prediction pipeline supports test-time augmentation and writes an averaged <code>InferenceStack</code> output that can also be used for uncertainty estimation.<br><br><b>How to cite:</b><br><cite>V. Boussot et al., <i>Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration</i>, arXiv preprint arXiv:2510.21358, <a href=\"https://doi.org/10.48550/arXiv.2510.21358\">https://doi.org/10.48550/arXiv.2510.21358</a>.</cite>",
    "tta": 2,
    "mc_dropout": false,
    "models": [
        "CV_0.pt",
        "CV_1.pt",
        "CV_2.pt",
        "CV_3.pt",
        "CV_4.pt"
    ],
    "inputs": {
        "CBCT": {
            "display_name": "CBCT",
            "volume_type": "VOLUME",
            "required": true
        }
    },
    "outputs": {
        "sCT": {
            "display_name": "sCT",
            "volume_type": "VOLUME",
            "required": true
        }
    },
    "inputs_evaluations": {
        "Image": {
            "Evaluation.yml": {
                "sCT": {
                    "display_name": "sCT",
                    "volume_type": "VOLUME",
                    "required": true
                },
                "CT": {
                    "display_name": "CT",
                    "volume_type": "VOLUME",
                    "required": true
                }
            }
        }
    },
    "vram_plan": {
        "8": {
            "patch_size": [
                1,
                512,
                512
            ],
            "batch_size": 16
        },
        "16": {
            "patch_size": [
                1,
                512,
                512
            ],
            "batch_size": 28
        },
        "24": {
            "patch_size": [
                1,
                512,
                512
            ],
            "batch_size": 48
        }
    },
    "task": "synthesis"
}