Update total_mr-3mm/metadata.json

total_mr-3mm/metadata.json

@@ -1,7 +1,7 @@
 {
-    "display_name": "Total MR 3mm",
-    "short_description": "",
-    "description": "",
+    "display_name": "TotalSegmentator MRI 3mm",
+    "short_description": "<b>Description:</b><br>Lightweight KonfAI adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator MRI</a>, enabling fast multimodal segmentation of <b>50 key anatomical structures</b> from <b>MRI and CT</b> scans at <b>3 mm</b> resolution, greatly reducing memory usage and inference time compared to the original nnU-Net workflow.<br><br><b>How to cite:</b><br><cite>T. Akinci D’Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
+    "description": "<b>Description:</b><br>This model integrates the reduced-resolution MRI-3mm configuration of TotalSegmentator into the <b>KonfAI</b> accelerated inference pipeline for efficient MRI/CT deployment.<br><br><b>Capabilities:</b><br>• Segmentation of <b>50 essential anatomical structures</b> (major organs, key bones, large vessels)<br>• <b>3 mm</b> isotropic input for high-throughput processing and lower GPU requirements<br>• Robust to acquisition variability including scanner type, contrast, and sequence parameters<br><br><b>Training data:</b><br>Trained on a diverse cohort of <b>1143 clinical scans</b> including <b>616 MRI</b> (multi-site, multi-scanner, multi-sequence) and <b>527 CT</b>, with expert-validated reference masks<br><br>><b>How to cite:</b><br><cite>T. Akinci D’Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
     "tta": 0,
     "mc_dropout": 0
 }