Update metadata.json

Signed-off-by: Foxconn-bgroup-aidd <[email protected]>

Author: Foxconn-bgroup-aidd

models/CoronSegmentator/configs/metadata.json

@@ -2,15 +2,15 @@
   "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
   "name": "coronaryArtery_ct_segmentation",
   "version": "1.0.0",
-  "monai_version": "1.4.0",
+  "monai_version": "1.3.2",
   "pytorch_version": "1.4.0",
   "numpy_version": "2.3.1",
   "required_packages_version": {
     "fire": "0.7.0",
-    "PyYAML": "6.0.2",
-    "gdown": "5.2.0",
+    "monai": " 1.4.0",
     "nnunetv2": "2.6.0",
     "nibabel": "5.3.2",
+    "numpy": "2.3.1",
     "numpy_stl": "3.2.0",
     "pynrrd": "1.1.3",
     "scipy": "1.16.0",
@@ -63,99 +63,6 @@
           "0": "Coronary Artery (1 = target, 0 = background)"
         }
       }
-    },
-
-    "optional_packages_version": {
-        "monai": "1.3.2",
-        "torch": "2.7.0+cu126",
-        "numpy": "2.3.1"
     }
   }
 }
-
-    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "name": "coronaryArtery_ct_segmentation",
-    "version": "1.0.0",
-    "monai_version": "1.4.0",
-    "pytorch_version": "1.4.0",
-    "numpy_version": "2.3.1",
-    "required_packages_version": {
-        "fire": "0.7.0",
-        "PyYAML": "6.0.2",
-        "gdown": "5.2.0",
-        "nnunetv2": "2.6.0",
-        "nibabel": "5.3.2",
-        "numpy_stl": "3.2.0",
-        "pynrrd": "1.1.3",
-        "scipy": "1.16.0",
-        "simpleitk": "2.5.0",
-        "scikit-image": "0.25.2",
-        "torch": "2.7.0+cu126",
-        "trimesh": "4.6.10",
-        "usd_core": "25.5.1",
-        "numpy-stl": "3.2.0"
-    },
-    "task": "Coronary artery ct segmentation",
-    "description": "Coronsegmentator is an automated pipeline that performs dual-task segmentation on cardiac CT images, focusing on both whole-heart and coronary artery structures. It integrates MONAI Auto3DSeg for general cardiac segmentation and a custom nnU-Net model for detailed coronary artery segmentation. The pipeline further converts segmentation results (in STL format) into USD files for downstream 3D visualization and digital twin simulation using NVIDIA Omniverse.",
-    "authors": "Y. Ke, MC. Chen, TY. Lin, YC. Chan Foxconn Digital Health AI Team",
-    "copyright": "Copyright \u00a9 2025 Hon Hai Precision Industry Co.,Ltd. All rights reserved",
-    "data_source": "The ImageCAS dataset is publicly available at: https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.git",
-    "data_type": "nifti",
-    "image_classes": "3D volume data",
-    "label_classes": "single channel data, 1 is coronary artery, 0 is background",
-    "pred_classes": "2 channels OneHot data, channel 1 is coronary artery, channel 0 is background",
-    "intended_use": "This is an example, not to be used for diagnostic purposes",
-    "references": [
-        "Zeng, An, et al. ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Computerized Medical Imaging and Graphics 109 (2023): 102287.",
-        "Isensee, Fabian, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18.2 (2021): 203-211."
-    ],
-    "network_data_format": {
-        "inputs": {
-            "image": {
-                "type": "image",
-                "format": "hounsfield",
-                "modality": "CT",
-                "num_channels": 1,
-                "spatial_shape": [
-                    512,
-                    512,
-                    256
-                ],
-                "dtype": "float32",
-                "value_range": [
-                    0,
-                    1
-                ],
-                "is_patch_data": true,
-                "channel_def": {
-                    "0": "image"
-                }
-            }
-        },
-        "outputs": {
-            "pred": {
-                "type": "usd",
-                "format": "segmentation",
-                "num_channels": 1,
-                "spatial_shape": [
-                    512,
-                    512,
-                    256
-                ],
-                "dtype": "float32",
-                "value_range": [
-                    0,
-                    1
-                ],
-                "channel_def": {
-                    "0": "Coronary Artery (1 = target, 0 = background)"
-                }
-            }
-        },
-        "optional_packages_version": {
-            "monai": "1.3.2",
-            "torch": "2.7.0+cu126",
-            "numpy": "2.3.1"
-        }
-    }
-}