refactor module

models/bamf_nnunet_mr_liver/config/default.yml

@@ -17,22 +17,20 @@ modules:
     import_dir: sorted_data
     sort_data: true
     meta: 
-      mod: 'mr'
+      mod: '%Modality'
 
   NiftiConverter:
     in_datas: dicom:mod=mr
-    engine: dcm2niix
+    engine: plastimatch
 
   NNUnetRunner:
     in_data: nifti:mod=mr
-    nnunet_task: 'Task822_Liver_AMD'
-    nnunet_model: '3d_fullres'
+    nnunet_task: Task822_Liver_AMD
+    nnunet_model: 3d_fullres
     roi: LIVER
 
-  BamfProcessorRunner:
-
   DsegConverter:
-    source_segs: 'nifti:mod=seg'
+    source_segs: nifti:mod=seg
     target_dicom: dicom:mod=mr
     model_name: 'Bamf NNUnet MR Liver'
     skip_empty_slices: True

models/bamf_nnunet_mr_liver/utils/BamfProcessorRunner.py

@@ -8,13 +8,12 @@
 -------------------------------------------------
 """
 
-from mhubio.core import Instance, InstanceData, DataType, FileType, CT, SEG
+from mhubio.core import Instance, InstanceData
 from mhubio.core import Module, IO
-import os, numpy as np
+import numpy as np
 import SimpleITK as sitk
-from skimage import measure, filters
+from skimage import measure
 import numpy as np
-import shutil
 
 
 
@@ -24,21 +23,22 @@ class BamfProcessorRunner(Module):
     @IO.Input('in_data', 'nifti:mod=ct|mr', the='input data to run nnunet on')
     @IO.Output('out_data', 'bamf_processed.nrrd', 'nrrd:mod=seg:processor=bamf', data='in_data', the="keep the two largest connected components of the segmentation and remove all other ones")
     def task(self, instance: Instance, in_data: InstanceData, out_data: InstanceData) -> None:
-       # Log bamf runner info
-        self.v("Running BamfProcessor on....")
-        self.v(f" > input data:  {in_data.abspath}")
-        self.v(f" > output data: {out_data.abspath}")
+
+        # Log bamf runner info
+        self.log("Running BamfProcessor on....")
+        self.log(f" > input data:  {in_data.abspath}")
+        self.log(f" > output data: {out_data.abspath}")
 
         # read image
-        self.v(f"Reading image from {in_data.abspath}")
+        self.log(f"Reading image from {in_data.abspath}")
         img_itk = sitk.ReadImage(in_data.abspath)
         img_np = sitk.GetArrayFromImage(img_itk)
 
         # apply post-processing
         img_bamf_processed = self.n_connected(img_np)
 
         # store image temporarily
-        self.v(f"Writing tmp image to {out_data.abspath}")
+        self.log(f"Writing tmp image to {out_data.abspath}")
         img_bamf_processed_itk = sitk.GetImageFromArray(img_bamf_processed)
         img_bamf_processed_itk.CopyInformation(img_itk)
         sitk.WriteImage(img_bamf_processed_itk, out_data.abspath)