add meta.json documentation reference

MHubAI · Nov 13, 2023 · 28a0bbd · 28a0bbd
1 parent 0388d5a
commit 28a0bbd
Showing 1 changed file with 135 additions and 0 deletions.
diff --git a/models/bamf_nnunet_ct_liver/meta.json b/models/bamf_nnunet_ct_liver/meta.json
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+{
+    "id": "",
+    "name": "bamf_nnunet_ct_liver",
+    "title": "AIMI CT Liver",
+    "summary": {
+        "description": "An nnU-Net based model to segment liver from CT scans",
+        "inputs": [
+            {
+                "label": "Input Image",
+                "description": "The CT scan of a patient.",
+                "format": "DICOM",
+                "modality": "CT",
+                "bodypartexamined": "LIVER",
+                "non-contrast": true,
+                "contrast": false
+            }
+        ],
+        "outputs": [
+            {
+                "label": "Segmentation",
+                "type": "Segmentation",
+                "description": "Segmentation liver",
+                "classes": [
+                    "LIVER"
+                ]
+            }
+        ],
+        "model": {
+            "architecture": "U-net",
+            "training": "Supervised",
+            "cmpapproach": "3D, ensemble"
+        },
+        "data": {
+            "training": {
+                "vol_samples": 1565
+            },
+            "evaluation": {
+                "vol_samples": 156
+            },
+            "public": true,
+            "external": true
+        }
+    },
+    "details": {
+        "name": "AIMI CT Liver",
+        "version": "1.0.0",
+        "devteam": "BAMF Health",
+        "authors": [
+            "Soni, Rahul",
+            "McCrumb, Diana",
+            "Murugesan, Gowtham Krishnan",
+            "Van Oss, Jeff"
+        ],
+        "type": "nnU-Net (U-Net structure, optimized by data-driven heuristics)",
+        "date": {
+            "code": "17.10.2023",
+            "weights": "28.08.2023",
+            "pub": "23.10.2023"
+        },
+        "licence": {
+            "code": "MIT",
+            "weights": "CC BY-NC 4.0"
+        },
+        "publications": [
+            {
+                "title": "The AIMI Initiative: AI-Generated Annotations in IDC Collections",
+                "uri": "https://arxiv.org/abs/2310.14897"
+            }
+        ],
+        "github": "https://github.com/bamf-health/aimi-liver-ct"
+    },
+    "info": {
+        "use": {
+            "title": "Intended Use",
+            "text": "This model is intended to perform liver segmentation in CT scans. The model has been trained and tested on scans aquired during clinical care of patients, so it might not be suited for a healthy population. The generalization capabilities of the model on a range of ages, genders, and ethnicities are unknown."
+        },
+        "analyses": {
+            "title": "Quantitative Analyses",
+            "text": "The model's performance was assessed using the Dice Coefficient and Normalized Surface Distance (NSD) with tolerance 7mm, as specified in the CT Liver segmentation task in the Medical Segmentation Decathlon challenge. The model was used to segment cases from the IDC collection TCGA-LIHC [1]. Seven of those cases were reviewed and corrected by a board-certified radiologist and a non-expert. The analysis is published here [2]",
+            "tables": [
+                {
+                    "label": "Label-wise metrics (mean (standard deviation)) between AI derived and manually corrected CT liver annotations",
+                    "entries": {
+                        "Dice: Radiologist": "0.91 (0.18)",
+                        "NSD: Radiologist": "0.89 (0.20)",
+                        "Dice: Non-expert": "0.90 (0.15)",
+                        "NSD: Non-expert": "0.85 (0.20)"
+                    }
+                }
+            ],
+            "references": [
+                {
+                    "label": "TCGA-LIHC",
+                    "uri": "https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436"
+                },
+                {
+                    "label": "The AIMI Initiative: AI-Generated Annotations for Imaging Data Commons Collections",
+                    "uri": "https://arxiv.org/abs/2310.14897"
+                }
+            ]
+        },
+        "evaluation": {
+            "title": "Evaluation Data",
+            "text": "The model was used to segment cases from the IDC [1] collection TCGA-LIHC [1]. Approximately 10% of those cases were randomly selected to be reviewed and corrected by a board-certified radiologist. The model predictions, and radiologist corrections are published on zenodo [3]",
+            "references": [
+                {
+                    "label": "Imaging Data Collections (IDC)",
+                    "uri": "https://datacommons.cancer.gov/repository/imaging-data-commons"
+                },
+                {
+                    "label": "TCGA-LIHC",
+                    "uri": "https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436"
+                },
+                {
+                    "label": "Image segmentations produced by the AIMI Annotations initiative",
+                    "uri": "https://zenodo.org/records/10009368"
+                }
+            ]
+        },
+        "training": {
+            "title": "Training Data",
+            "text": "The training dataset consists of  1565 CT liver annotations taken from TotalSegmentator [1] (N=1204) and Flare21 Liver Dataset [2] (N=361).",
+            "references": [
+                {
+                    "label": "TotalSegmentator Dataset",
+                    "uri": "https://zenodo.org/records/10047292"
+                },
+                {
+                    "label": "Flare21 Dataset",
+                    "uri": "https://zenodo.org/records/5903672"
+                }
+            ]
+        }
+    }
+}