ImpactSeg / body /app.json
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{
"display_name": "Segmentation: IMPACTSeg Body",
"short_description": "<b>Description:</b><br><b>IMPACTSeg</b> is a multimodal anatomical segmentation model for <b>CBCT, MR, and CT</b> scans. It predicts <b>11 labels</b> spanning soft tissues, cavities, bones, and central structures, and was trained on <b>232 CBCT + 282 MR + 955 CT</b> cases.",
"description": "<b>Description:</b><br><b>IMPACTSeg</b> is a multimodal anatomical segmentation model packaged for inference with <b>KonfAI</b>. It is designed for <b>CBCT, MR, and CT</b> scans and produces a consistent set of <b>11 labels</b> across modalities.<br><br><b>Training cohort:</b><br><b>232 CBCT + 282 MR + 955 CT</b> cases.<br><br><b>Use case:</b><br>Automated multimodal segmentation for downstream analysis, quantitative workflows, and clinical research pipelines.",
"tta": 0,
"mc_dropout": 0,
"models": ["Body.pt"],
"inputs": {
"Volume": {
"display_name": "Input Volume",
"volume_type": "VOLUME",
"required": true
}
},
"outputs": {
"Segmentation": {
"display_name": "Segmentation",
"volume_type": "SEGMENTATION",
"required": true
}
},
"inputs_evaluations": {
"Image": {
"Evaluation.yml": {
"Segmentation": {
"display_name": "Output Segmentation",
"volume_type": "VOLUME",
"required": true
},
"GT_Segmentation": {
"display_name": "GT Segmentation",
"volume_type": "VOLUME",
"required": true
}
}
}
},
"terminology": {
"1": { "name": "subcutaneous_tissue", "color": "#F4A261" },
"2": { "name": "muscle", "color": "#E76F51" },
"3": { "name": "abdominal_cavity", "color": "#2A9D8F" },
"4": { "name": "thoracic_cavity", "color": "#264653" },
"5": { "name": "bones", "color": "#E9C46A" },
"6": { "name": "gland_structure", "color": "#8AB17D" },
"7": { "name": "pericardium", "color": "#C8553D" },
"8": { "name": "prosthetic_breast_implant", "color": "#B56576" },
"9": { "name": "mediastinum", "color": "#577590" },
"10": { "name": "spinal_cord", "color": "#6D597A" },
"11": { "name": "brain", "color": "#43AA8B" }
}
}