ImpactSeg / body /app.json
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app.json: drop redundant top-level patch_size (now read from Prediction.yml)
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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"
}
},
"vram_plan": {
"8": {
"patch_size": [
1,
192,
192
],
"batch_size": 160
},
"16": {
"patch_size": [
1,
192,
192
],
"batch_size": 320
},
"24": {
"patch_size": [
1,
192,
192
],
"batch_size": 512
}
}
}