MR_CBCT/app.json

{
    "display_name": "Synthesis: MR/CBCT",
    "short_description": "Supervised whole-body MR/CBCT-to-sCT model.<br><br><b>Training data:</b><br>1734 paired cases from Task 1 + Task 2.<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 synthesis model distributed as a single checkpoint (<code>CV_0.pt</code>) for MR/CBCT-style inputs. The model follows the same 2.5D synthesis setup used in the ImpactSynth family and produces an sCT volume from a single input volume after automatic body-mask estimation and intensity normalization.<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>The model is trained on 1734 IMPACTReg-aligned paired synthesis cases spanning both Task 1 and Task 2 cohorts, combining MR and CBCT supervision in a unified synthesis setup.<br><br><b>Input handling:</b><br>The prediction pipeline accepts a single input volume exposed here as <code>MR or CBCT</code>, estimates a body mask automatically, resamples to the working resolution, applies normalization, and writes an averaged sCT output using test-time augmentation.<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": {
        "MR": {
            "display_name": "MR or 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
        }
    }
}