Add upload_annotation_volume

Add script and unit test

Rename dicom_utils to dicoms_to_volume.
  Add script
  Add unit test
Rename upload_annotations to upload_annotation_slice.
Update README.md
  How to use dicoms_to_volume
  How to use upload_annotation_volume

README.md

@@ -22,16 +22,38 @@ python -m mdai_utils.download_annotations \
  -o ./data
 ```
 
-## Upload annotations / segmentations
+## Upload 2D segmentations
 
 ```bash
-python -m mdai_utils.upload_annotations \
+python -m mdai_utils.upload_annotation_slice \
  --parameters ./tests/test_local_parameters.json \
  --sop_instance_uid "1.2.826.0.1.3680043.2.1125.1.75064541463040.2005072610414630768" \
  --label_name mylabel \
  -i ./tests/fixtures/humanct_0002_1000_seg.nii.gz
 ```
 
+## Upload 3D segmentations
+
+MDai works with dicoms, and use the SOPInstanceUID as the key to match slices.
+Your algorithm might work with 3D volumes, (.nrrd, .nii.gz, etc). You can convert the an input dicom_folder to a 3D volume, and also store the mapping between the new volume indices and the original dicom file, with its SOPInstanceUID.
+
+```bash
+python -m mdai_utils.dicom_to_volume -i ./tests/fixtures/humanct_0002_1000_1004 -o /tmp/humanct_0002_1000_1004.nrrd
+```
+
+Parallel to the output image location, a `{image_filename}_SOPInstanceUIDs.json` will be saved with the slice
+mappings.
+
+If we have a 3D volume segmentation we want to upload, use the mappings:
+
+```bash
+python -m mdai_utils.upload_annotation_volume \
+ --parameters ./tests/test_local_parameters.json \
+ --sop_instance_uids_file ./tests/fixtures/humanct_0002_1000_1004_SOPInstanceUIDs.json \
+ --label_name mylabel \
+ -i ./tests/fixtures/humanct_0002_1000_1004_seg.nii.gz
+```
+
 ## Development
 
 For information about building, running, and contributing to this code base,

mdai_utils/dicom_utils.py → mdai_utils/dicoms_to_volume.py

@@ -27,7 +27,12 @@ class ImageAndMetadata(NamedTuple):
     metadata: dict
 
 
-def read_dicoms_into_volume(valid_dcm_file_list) -> ImageAndMetadata:
+def metadata_dict_to_sop_instance_uids(metadata_dict):
+    """Convert a metadata dictionary to a dictionary mapping slice index to SOPInstanceUID."""
+    return {int(k): v["SOPInstanceUID"] for k, v in metadata_dict.items()}
+
+
+def dicoms_to_volume(valid_dcm_file_list) -> ImageAndMetadata:
     """Convert a list of DICOM files to a image volume. Also returns metadata
     (SOPInstanceUID) for each slice in the volume.
 
@@ -58,3 +63,43 @@ def read_dicoms_into_volume(valid_dcm_file_list) -> ImageAndMetadata:
             for slice_idx, (fn, uid) in enumerate(zip(dicom_names, uids))
         }
         return ImageAndMetadata(image=image, metadata=metadata)
+
+
+def _get_parser():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-i",
+        "--input_directory",
+        type=str,
+        required=True,
+        help="Path to the directory containing the DICOM files.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output_image",
+        type=str,
+        required=True,
+        help="Path to the output image.",
+    )
+    return parser
+
+
+def main(input_directory, output_image):
+    """
+    Convert a directory containing DICOM files to a volume image, and save the metadata to map SOPInstanceUID to slice indices.
+    """
+    valid_dcm_file_list = file_list_from_directory(input_directory)
+    image_and_metadata = dicoms_to_volume(valid_dcm_file_list)
+    write_image_and_metadata(
+        image=image_and_metadata.image,
+        metadata=image_and_metadata.metadata,
+        output_image_filename=output_image,
+    )
+
+
+if __name__ == "__main__":
+    parser = _get_parser()
+    args = parser.parse_args()
+    main(args.input_directory, args.output_image)

mdai_utils/upload_annotations.py → mdai_utils/upload_annotation_slice.py

@@ -3,6 +3,15 @@
 import numpy as np
 
 
+def read_data_image(image_path):
+    """
+    Read an image using itk, and returns a numpy data array.
+    """
+    image = itk.imread(image_path)
+    data_np = itk.array_from_image(image)
+    return data_np
+
+
 def upload_data_annotation_slice(
     data_np: np.ndarray,
     sop_instance_uid: str,
@@ -36,15 +45,6 @@ def upload_data_annotation_slice(
     return failed_annotations
 
 
-def read_data_image(image_path):
-    """
-    Read an image using itk, and returns a numpy data array.
-    """
-    image = itk.imread(image_path)
-    data_np = itk.array_from_image(image)
-    return data_np
-
-
 def upload_image_annotation_slice(
     segmentation_image_path: str,
     sop_instance_uid: str,
@@ -56,9 +56,10 @@ def upload_image_annotation_slice(
     """
     Uploads an annotation to the server. It requires that the DICOM image is already
     uploaded to the server. See upload_dicom_image.py for that.
+    The input image can be in any format supported by ITK.
 
     Args:
-        segmentation_image_path (str): Path to the segmentation image. With fixed metadata. See @inverse_transform
+        segmentation_image_path (str): Path to the segmentation image.
         sop_instance_uid (str): SOPInstanceUID of the DICOM key-slice image. Returned from @inverse_transform
         mdai_client (mdai.Client): Client to the MD.ai API. See @get_mdai_client
         mdai_project_id (str): Project ID. Check in the MD.ai web interface.
@@ -74,11 +75,11 @@ def upload_image_annotation_slice(
         data_np = data_np.squeeze(0)
     return upload_data_annotation_slice(
         data_np=data_np,
+        sop_instance_uid=sop_instance_uid,
         mdai_client=mdai_client,
         mdai_project_id=mdai_project_id,
         mdai_dataset_id=mdai_dataset_id,
         mdai_label_id=mdai_label_id,
-        sop_instance_uid=sop_instance_uid,
     )
 
 
@@ -106,12 +107,6 @@ def _get_parser():
         default=None,
         help="sop_instance_uid of the annotation file. Needed to match the annotation with the DICOM image in mdai.",
     )
-    parser.add_argument(
-        "--sop_instance_metadata",
-        type=str,
-        default=None,
-        help="json file generated by storing the metadata resulting from dicom_utils.read_dicoms_into_volume",
-    )
     parser.add_argument(
         "-p",
         "--parameters",
@@ -126,6 +121,27 @@ def _get_parser():
     return parser
 
 
+def main(
+    input_annotation,
+    label_name,
+    sop_instance_uid,
+    mdai_client,
+    mdai_project_id,
+    mdai_dataset_id,
+    mdai_label_ids,
+):
+    mdai_label_id = mdai_label_ids[label_name]
+    failed_annotations = upload_image_annotation_slice(
+        segmentation_image_path=input_annotation,
+        sop_instance_uid=sop_instance_uid,
+        mdai_client=mdai_client,
+        mdai_project_id=mdai_project_id,
+        mdai_dataset_id=mdai_dataset_id,
+        mdai_label_id=mdai_label_id,
+    )
+    return failed_annotations
+
+
 if __name__ == "__main__":
     import json
 
@@ -140,30 +156,30 @@ def _get_parser():
 
     mdai_project_id = parameters["mdai_project_id"]
     mdai_dataset_id = parameters["mdai_dataset_id"]
-    mdai_label_group_id = parameters["mdai_label_group_id"]
     mdai_label_ids = parameters["mdai_label_ids"]
     mdai_domain = parameters["mdai_domain"]
 
     input_annotation = args.input_annotation
     label_name = args.label_name
     mdai_label_id = mdai_label_ids[label_name]
     sop_instance_uid = args.sop_instance_uid
-    sop_instance_metadata = args.sop_instance_metadata
     if sop_instance_uid is None:
         raise ValueError(
-            "Either sop_instance_uid or sop_instance_metadata must be set."
+            "sop_instance_uid is required to match the annotation with the DICOM image in mdai."
         )
     token = get_mdai_access_token()
     mdai_client = mdai.Client(domain=mdai_domain, access_token=token)
 
-    failed_annotations = upload_image_annotation_slice(
-        segmentation_image_path=input_annotation,
+    failed_annotations = main(
+        input_annotation=input_annotation,
+        label_name=label_name,
         sop_instance_uid=sop_instance_uid,
         mdai_client=mdai_client,
         mdai_project_id=mdai_project_id,
         mdai_dataset_id=mdai_dataset_id,
-        mdai_label_id=mdai_label_id,
+        mdai_label_ids=mdai_label_ids,
     )
+
     if len(failed_annotations) == 0:
         print("All annotations uploaded successfully.")
         exit(0)