update meta info

MHubAI · Jul 3, 2024 · b964f5f · b964f5f
1 parent 7ec991b
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Showing 3 changed files with 7 additions and 3 deletions.
diff --git a/models/bamf_ct_liver_tumor/config/default.yml b/models/bamf_ct_liver_tumor/config/default.yml
@@ -23,7 +23,7 @@ modules:
     in_data: nifti:mod=ct
     nnunet_dataset: Dataset006_Liver
     nnunet_config: 3d_fullres
-    roi: SPLEEN,KIDNEY,GALLBLADDER,DUODENUM,PANCREAS,SMALL_INTESTINE,LUNG,LIVER,LIVER+TUMOR
+    roi: SPLEEN,KIDNEY,GALLBLADDER,DUODENUM,PANCREAS,SMALL_INTESTINE,LUNG,LIVER,LIVER+NEOPLASM_MALIGNANT_PRIMARY
     export_prob_maps: False
 
   DsegConverter:

diff --git a/models/bamf_ct_liver_tumor/meta.json b/models/bamf_ct_liver_tumor/meta.json
@@ -64,13 +64,17 @@
       "code": "MIT",
       "weights": "CC BY-NC 4.0"
     },
-    "github": "https://github.com/MHubAI/models/blob/main/models/bamf_ct_liver_tumor"
+    "github": "https://github.com/MHubAI/models"
   },
   "info": {
     "use": {
       "title": "Intended Use",
       "text": "This model is intended to perform liver and tumor segmentation in CT scans. The liver is a common site of primary (i.e. originating in the liver like hepatocellular carcinoma, HCC) or secondary (i.e. spreading to the liver like colorectal cancer) tumor development.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 to be assessed using the Dice Coefficient"
+    },
     "evaluation": {
       "title": "Evaluation Data",
       "text": "The model was used to segment cases 509 from the Colorectal-Liver-Metastases [1] collection HCC-TACE-Seg [2]. 52 of those cases were randomly selected to be reviewed and corrected by a board-certified radiologist.",

diff --git a/models/bamf_ct_liver_tumor/utils/PostProcessor.py b/models/bamf_ct_liver_tumor/utils/PostProcessor.py
@@ -51,7 +51,7 @@ def n_connected(self, img_data: np.ndarray) -> np.ndarray:
 
     @IO.Instance()
     @IO.Input('in_data', 'nifti:mod=seg:model=nnunet', the='input segmentations')
-    @IO.Output('out_data', 'bamf_processed..nii.gz', 'nifti:mod=seg:processor=bamf:roi=LIVER,LIVER+TUMOR', data='in_data', the="filtered Liver and tumor segmentation")
+    @IO.Output('out_data', 'bamf_processed..nii.gz', 'nifti:mod=seg:processor=bamf:roi=LIVER,LIVER+NEOPLASM_MALIGNANT_PRIMARY', data='in_data', the="filtered Liver and tumor segmentation")
     def task(self, instance: Instance, in_data: InstanceData, out_data: InstanceData) -> None:
 
         # Log bamf runner info