Update CBCT/app.json

CBCT/app.json

@@ -1,6 +1,6 @@
 {
     "display_name": "Synthesis: CBCT",
-    "short_description": "<b>Description:</b><br>Supervised CBCT synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 2)</a>.<br><b>⚠️ Warning:</b><br> The models were <b>trained using an anatomical mask</b>. However, <b>no mask is provided at inference time</b>. As a result, you may observe <b>artifacts or unreliable predictions outside the anatomical region of interest</b>.<br><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>",
+    "short_description": "<b>Description:</b><br>Supervised CBCT synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 2)</a>.<br><b>⚠️ Warning:</b> Models were trained with an anatomical mask, but no mask is used at inference. Artifacts may appear outside the anatomy. Future models will be trained without masks.<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 CBCT synthesis model developed as part of the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 2)</a>.<br><br><b>Architecture:</b><br>Based on a 2.5D UNet++ with a ResNet34 encoder, the model was optimized using the <b>IMPACT-Synth loss</b>, a perceptual loss leveraging semantic priors from <b>SAM 2.1-s</b>. Training was conducted with the <b>KonfAI</b> deep learning framework.<br><br><b>Training data:</b><br>Paired CT–CBCT volumes from the <a href=\"https://synthrad2025.grand-challenge.org/\">SynthRAD 2025 Challenge (Task 2)</a>, <b>aligned using IMPACT-based registration</b>. Corresponding B-spline deformation fields are available in the <a href=\"https://huggingface.co/datasets/VBoussot/synthrad2025-impact-registration\">SynthRAD2025-IMPACT (aligned)</a> dataset repository.<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,